windmill/backend/windmill-api/src/mcp/session/gen/instructions.json

{
	"index": {
		"script": {
			"": "script",
			"python3": "script.python3",
			"deno": "script.deno",
			"go": "script.go",
			"bash": "script.bash",
			"powershell": "script.powershell",
			"postgresql": "script.postgresql",
			"mysql": "script.mysql",
			"bigquery": "script.bigquery",
			"snowflake": "script.snowflake",
			"mssql": "script.mssql",
			"oracledb": "script.oracledb",
			"graphql": "script.graphql",
			"nativets": "script.nativets",
			"bun": "script.bun",
			"php": "script.php",
			"rust": "script.rust",
			"ansible": "script.ansible",
			"csharp": "script.csharp",
			"nu": "script.nu",
			"java": "script.java",
			"ruby": "script.ruby",
			"rlang": "script.rlang",
			"duckdb": "script.duckdb",
			"bunnative": "script.bunnative",
			"dbt": "script.dbt"
		},
		"flow": {
			"": "flow",
			"python3": "flow",
			"deno": "flow",
			"go": "flow",
			"bash": "flow",
			"powershell": "flow",
			"postgresql": "flow",
			"mysql": "flow",
			"bigquery": "flow",
			"snowflake": "flow",
			"mssql": "flow",
			"oracledb": "flow",
			"graphql": "flow",
			"nativets": "flow",
			"bun": "flow",
			"php": "flow",
			"rust": "flow",
			"ansible": "flow",
			"csharp": "flow",
			"nu": "flow",
			"java": "flow",
			"ruby": "flow",
			"rlang": "flow",
			"duckdb": "flow",
			"bunnative": "flow",
			"dbt": "flow"
		},
		"resource": {
			"": "resource",
			"python3": "resource",
			"deno": "resource",
			"go": "resource",
			"bash": "resource",
			"powershell": "resource",
			"postgresql": "resource",
			"mysql": "resource",
			"bigquery": "resource",
			"snowflake": "resource",
			"mssql": "resource",
			"oracledb": "resource",
			"graphql": "resource",
			"nativets": "resource",
			"bun": "resource",
			"php": "resource",
			"rust": "resource",
			"ansible": "resource",
			"csharp": "resource",
			"nu": "resource",
			"java": "resource",
			"ruby": "resource",
			"rlang": "resource",
			"duckdb": "resource",
			"bunnative": "resource",
			"dbt": "resource"
		},
		"app": {
			"": "app",
			"python3": "app.python3",
			"deno": "app",
			"go": "app",
			"bash": "app",
			"powershell": "app",
			"postgresql": "app",
			"mysql": "app",
			"bigquery": "app",
			"snowflake": "app",
			"mssql": "app",
			"oracledb": "app",
			"graphql": "app",
			"nativets": "app",
			"bun": "app",
			"php": "app",
			"rust": "app",
			"ansible": "app",
			"csharp": "app",
			"nu": "app",
			"java": "app",
			"ruby": "app",
			"rlang": "app",
			"duckdb": "app",
			"bunnative": "app",
			"dbt": "app"
		},
		"datatable": {
			"": "datatable",
			"python3": "datatable.python3",
			"deno": "datatable",
			"go": "datatable.go",
			"bash": "datatable.go",
			"powershell": "datatable.go",
			"postgresql": "datatable.go",
			"mysql": "datatable.go",
			"bigquery": "datatable.go",
			"snowflake": "datatable.go",
			"mssql": "datatable.go",
			"oracledb": "datatable.go",
			"graphql": "datatable.go",
			"nativets": "datatable",
			"bun": "datatable",
			"php": "datatable.go",
			"rust": "datatable.go",
			"ansible": "datatable.go",
			"csharp": "datatable.go",
			"nu": "datatable.go",
			"java": "datatable.go",
			"ruby": "datatable.go",
			"rlang": "datatable.go",
			"duckdb": "datatable.go",
			"bunnative": "datatable",
			"dbt": "datatable.go"
		},
		"pipeline": {
			"": "pipeline",
			"python3": "pipeline",
			"deno": "pipeline",
			"go": "pipeline",
			"bash": "pipeline",
			"powershell": "pipeline",
			"postgresql": "pipeline",
			"mysql": "pipeline",
			"bigquery": "pipeline",
			"snowflake": "pipeline",
			"mssql": "pipeline",
			"oracledb": "pipeline",
			"graphql": "pipeline",
			"nativets": "pipeline",
			"bun": "pipeline",
			"php": "pipeline",
			"rust": "pipeline",
			"ansible": "pipeline",
			"csharp": "pipeline",
			"nu": "pipeline",
			"java": "pipeline",
			"ruby": "pipeline",
			"rlang": "pipeline",
			"duckdb": "pipeline",
			"bunnative": "pipeline",
			"dbt": "pipeline"
		},
		"workspace_settings": {
			"": "workspace_settings",
			"python3": "workspace_settings",
			"deno": "workspace_settings",
			"go": "workspace_settings",
			"bash": "workspace_settings",
			"powershell": "workspace_settings",
			"postgresql": "workspace_settings",
			"mysql": "workspace_settings",
			"bigquery": "workspace_settings",
			"snowflake": "workspace_settings",
			"mssql": "workspace_settings",
			"oracledb": "workspace_settings",
			"graphql": "workspace_settings",
			"nativets": "workspace_settings",
			"bun": "workspace_settings",
			"php": "workspace_settings",
			"rust": "workspace_settings",
			"ansible": "workspace_settings",
			"csharp": "workspace_settings",
			"nu": "workspace_settings",
			"java": "workspace_settings",
			"ruby": "workspace_settings",
			"rlang": "workspace_settings",
			"duckdb": "workspace_settings",
			"bunnative": "workspace_settings",
			"dbt": "workspace_settings"
		}
	},
	"texts": {
		"script": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n- No script language was provided. Default to `bun` only for new TypeScript scripts; if the user requested another language or you read an existing script, call get_instructions again with that language.\n\n# Windmill script authoring reference (bun)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# TypeScript (Bun)\n\nBun runtime with full npm ecosystem and fastest execution. **Bun is the default and preferred TypeScript runtime** — choose it for any TypeScript script unless there is a major reason to use Deno for that specific use-case.\n\n## Structure\n\nExport a single **async** function called `main`:\n\n```typescript\nexport async function main(param1: string, param2: number) {\n  // Your code here\n  return { result: param1, count: param2 };\n}\n```\n\nDo not call the main function. Libraries are installed automatically.\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nUse the `RT` namespace for resource types:\n\n```typescript\nexport async function main(stripe: RT.Stripe) {\n  // stripe contains API key and config from the resource\n}\n```\n\nOnly use resource types if you need them to satisfy the instructions. Always use the RT namespace.\n\n## Imports\n\n```typescript\nimport Stripe from \"stripe\";\nimport { someFunction } from \"some-package\";\n```\n\n## Prefer `//native` when the runtime allows it\n\nIf a script only needs `fetch` and the JavaScript standard library — including when it uses `windmill-client` — prefer making it a **native** script: add `//native` as the first line and write it with the `write-script-bunnative` skill. Native scripts run on a lightweight V8 isolate, start faster, and parallelize heavily. `windmill-client` works on the native worker (its calls go over `fetch`), so needing the Windmill client is **not** a reason to avoid `//native`. Use the regular `bun` language only when the code (or a dependency) needs Node/Bun runtime APIs — `node:*` modules, the filesystem, child processes, or native addons.\n\n## Windmill Client\n\nImport the windmill client for platform interactions:\n\n```typescript\nimport * as wmill from \"windmill-client\";\n```\n\n**Prefer `windmill-client` over raw `fetch` for anything that talks to Windmill** — reading resources/variables/states, running scripts and flows, S3 object operations, etc. It handles auth, the workspace, and the base URL for you, so you don't hand-roll URLs or tokens. Reserve `fetch` for calling *external* HTTP APIs that aren't Windmill.\n\nThe full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method to use instead of guessing or falling back to `fetch`.\n\n## Preprocessor Scripts\n\nFor preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:\n\n```typescript\ntype Event = {\n  kind:\n    | \"webhook\"\n    | \"http\"\n    | \"websocket\"\n    | \"kafka\"\n    | \"email\"\n    | \"nats\"\n    | \"postgres\"\n    | \"sqs\"\n    | \"mqtt\"\n    | \"amqp\"\n    | \"gcp\"\n    | \"azure\";\n  body: any;\n  headers: Record<string, string>;\n  query: Record<string, string>;\n};\n\nexport async function preprocessor(event: Event) {\n  return {\n    param1: event.body.field1,\n    param2: event.query.id,\n  };\n}\n```\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.\n\n### Receiving an S3Object as a script parameter\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\nexport async function main(file: wmill.S3Object) {\n  const content = await wmill.loadS3File(file);\n  // ...\n}\n```\n\n### S3 operations\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\n// Load file content from S3\nconst content: Uint8Array = await wmill.loadS3File(s3object);\n\n// Load file as stream\nconst blob: Blob = await wmill.loadS3FileStream(s3object);\n\n// Write file to S3\nconst result: wmill.S3Object = await wmill.writeS3File(\n  s3object, // Target path (or undefined to auto-generate)\n  fileContent, // string or Blob\n  s3ResourcePath // Optional: specific S3 resource to use\n);\n```\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"script.python3": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (python3)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Python\n\n## Structure\n\nThe script must contain at least one function called `main`:\n\n```python\ndef main(param1: str, param2: int):\n    # Your code here\n    return {\"result\": param1, \"count\": param2}\n```\n\nDo not call the main function. Libraries are installed automatically.\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nYou need to **redefine** the type of the resources that are needed before the main function as TypedDict:\n\n```python\nfrom typing import TypedDict\n\nclass postgresql(TypedDict):\n    host: str\n    port: int\n    user: str\n    password: str\n    dbname: str\n\ndef main(db: postgresql):\n    # db contains the database connection details\n    pass\n```\n\n**Important rules:**\n\n- The resource type name must be **IN LOWERCASE**\n- Only include resource types if they are actually needed\n- If an import conflicts with a resource type name, **rename the imported object, not the type name**\n- Make sure to import TypedDict from typing **if you're using it**\n\n## Imports\n\nLibraries are installed automatically. Do not show installation instructions.\n\n```python\nimport requests\nimport pandas as pd\nfrom datetime import datetime\n```\n\nIf an import name conflicts with a resource type:\n\n```python\n# Wrong - don't rename the type\nimport stripe as stripe_lib\nclass stripe_type(TypedDict): ...\n\n# Correct - rename the import\nimport stripe as stripe_sdk\nclass stripe(TypedDict):\n    api_key: str\n```\n\n## Windmill Client\n\nImport the windmill client for platform interactions:\n\n```python\nimport wmill\n```\n\nSee the SDK documentation for available methods.\n\n## Preprocessor Scripts\n\nFor preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:\n\n```python\nfrom typing import TypedDict, Literal, Any\n\nclass Event(TypedDict):\n    kind: Literal[\"webhook\", \"http\", \"websocket\", \"kafka\", \"email\", \"nats\", \"postgres\", \"sqs\", \"mqtt\", \"amqp\", \"gcp\", \"azure\"]\n    body: Any\n    headers: dict[str, str]\n    query: dict[str, str]\n\ndef preprocessor(event: Event):\n    # Transform the event into flow input parameters\n    return {\n        \"param1\": event[\"body\"][\"field1\"],\n        \"param2\": event[\"query\"][\"id\"]\n    }\n```\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations.\n\n### Receiving an S3Object as a script parameter\n\nTo accept a file from S3 as input to a script, type the parameter with `S3Object` (imported from `wmill`):\n\n```python\nimport wmill\nfrom wmill import S3Object\n\ndef main(file: S3Object):\n    content = wmill.load_s3_file(file)\n    # ...\n```\n\n### S3 operations\n\n```python\nimport wmill\n\n# Load file content from S3\ncontent: bytes = wmill.load_s3_file(s3object)\n\n# Load file as stream reader\nreader: BufferedReader = wmill.load_s3_file_reader(s3object)\n\n# Write file to S3\nresult: S3Object = wmill.write_s3_file(\n    s3object,           # Target path (or None to auto-generate)\n    file_content,       # bytes or BufferedReader\n    s3_resource_path,   # Optional: specific S3 resource\n    content_type,       # Optional: MIME type\n    content_disposition # Optional: Content-Disposition header\n)\n```\n\n\n# Python SDK (wmill)\n\nImport: import wmill\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe functions below are the surface to prefer. For an endpoint none of them covers,\nwmill.Windmill().get(endpoint) and .post(endpoint) issue an authenticated request against this\ninstance. What does not exist is a function name you guessed at: if it is not listed below, do\nnot call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`os.environ.get(\"WM_END_USER_EMAIL\") or os.environ.get(\"WM_EMAIL\")`. WM_END_USER_EMAIL is the app\nviewer when the run was triggered from an app and empty otherwise (both variables are always\ndefined), WM_EMAIL is the user the job is permissioned as. WM_USERNAME is the matching username.\n\ndef worker_has_internal_server() -> bool\n\ndef get_mocked_api() -> Optional[dict]\n\n# Get the HTTP client instance.\n# \n# Returns:\n#     Configured httpx.Client for API requests\ndef get_client() -> httpx.Client\n\n# Make an HTTP GET request to the Windmill API.\n# \n# Args:\n#     endpoint: API endpoint path\n#     raise_for_status: Whether to raise an exception on HTTP errors\n#     **kwargs: Additional arguments passed to httpx.get\n# \n# Returns:\n#     HTTP response object\ndef get(endpoint, raise_for_status = True, **kwargs) -> httpx.Response\n\n# Make an HTTP POST request to the Windmill API.\n# \n# Args:\n#     endpoint: API endpoint path\n#     raise_for_status: Whether to raise an exception on HTTP errors\n#     **kwargs: Additional arguments passed to httpx.post\n# \n# Returns:\n#     HTTP response object\ndef post(endpoint, raise_for_status = True, **kwargs) -> httpx.Response\n\n# Create a new authentication token.\n# \n# Args:\n#     duration: Token validity duration (default: 1 day)\n# \n# Returns:\n#     New authentication token string\ndef create_token(duration = dt.timedelta(days=1)) -> str\n\n# Create a script job by path and return its job id.\n# \n# ``retry`` re-runs the job when it fails, with the policy a schedule takes,\n# e.g. ``{\"constant\": {\"attempts\": 3, \"seconds\": 10}}``. The returned id is\n# the first attempt's; each retry is a job of its own, recorded as a child of it.\ndef run_script_by_path_async(path: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None, retry: dict = None) -> str\n\n# Create a script job by hash and return its job id. See ``run_script_by_path_async`` for ``retry``.\ndef run_script_by_hash_async(hash_: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None, retry: dict = None) -> str\n\n# Create a flow job and return its job id.\ndef run_flow_async(path: str, args: dict = None, scheduled_in_secs: int = None, do_not_track_in_parent: bool = True, tag: str = None) -> str\n\n# Run script by path synchronously and return its result.\ndef run_script_by_path(path: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any\n\n# Run script by hash synchronously and return its result.\ndef run_script_by_hash(hash_: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any\n\n# Run a script on the current worker without creating a job.\n# \n# On agent workers (no internal server), falls back to running a normal\n# preview job and waiting for the result.\ndef run_inline_script_preview(content: str, language: str, args: dict = None) -> Any\n\n# Wait for a job to complete and return its result.\n# \n# Args:\n#     job_id: ID of the job to wait for\n#     timeout: Maximum time to wait (seconds or timedelta)\n#     verbose: Enable verbose logging\n#     cleanup: Register cleanup handler to cancel job on exit\n#     assert_result_is_not_none: Raise exception if result is None\n# \n# Returns:\n#     Job result when completed\n# \n# Raises:\n#     TimeoutError: If timeout is reached\n#     Exception: If job fails\ndef wait_job(job_id, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False)\n\n# Cancel a specific job by ID.\n# \n# Args:\n#     job_id: UUID of the job to cancel\n#     reason: Optional reason for cancellation\n# \n# Returns:\n#     Response message from the cancel endpoint\ndef cancel_job(job_id: str, reason: str = None) -> str\n\n# Cancel currently running executions of the same script.\ndef cancel_running() -> dict\n\n# Get job details by ID.\n# \n# Args:\n#     job_id: UUID of the job\n# \n# Returns:\n#     Job details dictionary\ndef get_job(job_id: str) -> dict\n\n# Get the root job ID for a flow hierarchy.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     Root job ID\ndef get_root_job_id(job_id: str | None = None) -> dict\n\n# Get an OIDC JWT token for authentication to external services.\n# \n# Args:\n#     audience: Token audience (e.g., \"vault\", \"aws\")\n#     expires_in: Optional expiration time in seconds\n# \n# Returns:\n#     JWT token string\ndef get_id_token(audience: str, expires_in: int | None = None) -> str\n\n# Get the status of a job.\n# \n# Args:\n#     job_id: UUID of the job\n# \n# Returns:\n#     Job status: \"RUNNING\", \"WAITING\", or \"COMPLETED\"\ndef get_job_status(job_id: str) -> JobStatus\n\n# Get the result of a completed job.\n# \n# Args:\n#     job_id: UUID of the completed job\n#     assert_result_is_not_none: Raise exception if result is None\n# \n# Returns:\n#     Job result\ndef get_result(job_id: str, assert_result_is_not_none: bool = True) -> Any\n\n# Get a variable value by path.\n# \n# Args:\n#     path: Variable path in Windmill\n# \n# Returns:\n#     Variable value as string\ndef get_variable(path: str) -> str\n\n# Set a variable value by path, creating it if it doesn't exist.\n# \n# Args:\n#     path: Variable path in Windmill\n#     value: Variable value to set\n#     is_secret: Whether the variable should be secret (default: False)\ndef set_variable(path: str, value: str, is_secret: bool = False) -> None\n\n# Get a resource value by path.\n# \n# Args:\n#     path: Resource path in Windmill\n#     none_if_undefined: Return None instead of raising if not found\n#     interpolated: if variables and resources are fully unrolled\n# \n# Returns:\n#     Resource value dictionary or None\ndef get_resource(path: str, none_if_undefined: bool = False, interpolated: bool = True) -> dict | None\n\n# Set a resource value by path, creating it if it doesn't exist.\n# \n# Args:\n#     value: Resource value to set\n#     path: Resource path in Windmill\n#     resource_type: Resource type for creation\ndef set_resource(value: Any, path: str, resource_type: str)\n\n# List resources from Windmill workspace.\n# \n# Args:\n#     resource_type: Optional resource type to filter by (e.g., \"postgresql\", \"mysql\", \"s3\")\n#     page: Optional page number for pagination\n#     per_page: Optional number of results per page\n#     \n# Returns:\n#     List of resource dictionaries\ndef list_resources(resource_type: str = None, page: int = None, per_page: int = None) -> list[dict]\n\n# Set the workflow state.\n# \n# Args:\n#     value: State value to set\n#     path: Optional state resource path override.\ndef set_state(value: Any, path: str | None = None) -> None\n\n# Get the workflow state.\n# \n# Args:\n#     path: Optional state resource path override.\n# \n# Returns:\n#     State value or None if not set\ndef get_state(path: str | None = None) -> Any\n\n# Set job progress percentage (0-99).\n# \n# Args:\n#     value: Progress percentage\n#     job_id: Job ID (defaults to current WM_JOB_ID)\ndef set_progress(value: int, job_id: Optional[str] = None)\n\n# Get job progress percentage.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     Progress value (0-100) or None if not set\ndef get_progress(job_id: Optional[str] = None) -> Any\n\n# Whether the worker running the job has received its shutdown signal.\n# \n# A draining worker waits for its current job to finish before exiting. A long-running\n# script can poll this and return early so the worker can be replaced.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     True if the worker running the job is draining\ndef worker_is_draining(job_id: Optional[str] = None) -> bool\n\n# Set the user state of a flow at a given key\ndef set_flow_user_state(key: str, value: Any) -> None\n\n# Get the user state of a flow at a given key\ndef get_flow_user_state(key: str) -> Any\n\n# Get the Windmill server version.\n# \n# Returns:\n#     Version string\ndef version()\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from DuckDB.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: address it as ``s3://<bucket>/<key>`` with the\n# storage's own bucket, returned as ``s3_bucket``. Pass the resource path to connect with\n# the storage's own credentials instead.\ndef get_duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings | None\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from Polars.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: address it as ``s3://<bucket>/<key>`` with the\n# storage's own bucket, returned as ``s3_bucket``. Pass the resource path to connect with\n# the storage's own credentials instead.\ndef get_polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection using boto3.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: pass the storage's own bucket as ``Bucket``. The proxy\n# serves object reads, writes, deletes, listing and multipart uploads of that bucket only;\n# presigned URLs, other buckets and bucket-level APIs need the storage's own credentials,\n# which passing the resource path returns.\ndef get_boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings\n\n# Load a file from the workspace s3 bucket and returns its content as bytes.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# my_obj_content = client.load_s3_file(s3_obj)\n# file_content = my_obj_content.decode(\"utf-8\")\n# '''\ndef load_s3_file(s3object: S3Object | str, s3_resource_path: str | None) -> bytes\n\n# Load a file from the workspace s3 bucket and returns the bytes stream.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# with wmill.load_s3_file_reader(s3object, s3_resource_path) as file_reader:\n#     print(file_reader.read())\n# '''\ndef load_s3_file_reader(s3object: S3Object | str, s3_resource_path: str | None) -> BufferedReader\n\n# Write a file to the workspace S3 bucket\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# \n# # for an in memory bytes array:\n# file_content = b'Hello Windmill!'\n# client.write_s3_file(s3_obj, file_content)\n# \n# # for a file:\n# with open(\"my_file.txt\", \"rb\") as my_file:\n#     client.write_s3_file(s3_obj, my_file)\n# '''\ndef write_s3_file(s3object: S3Object | str | None, file_content: BufferedReader | bytes, s3_resource_path: str | None, content_type: str | None = None, content_disposition: str | None = None) -> S3Object\n\n# Permanently delete a file from the workspace S3 bucket.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# client.delete_s3_object(s3_obj)\n# '''\ndef delete_s3_object(s3object: S3Object | str, s3_resource_path: str | None = None) -> None\n\n# Sign S3 objects for use by anonymous users in public apps.\n# \n# Args:\n#     s3_objects: List of S3 objects to sign\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     List of signed S3 objects\ndef sign_s3_objects(s3_objects: list[S3Object | str], expiry_secs: int | None = None) -> list[S3Object]\n\n# Sign a single S3 object for use by anonymous users in public apps.\n# \n# Args:\n#     s3_object: S3 object to sign\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     Signed S3 object\ndef sign_s3_object(s3_object: S3Object | str, expiry_secs: int | None = None) -> S3Object\n\n# Generate presigned public URLs for an array of S3 objects.\n# If an S3 object is not signed yet, it will be signed first.\n# \n# Args:\n#     s3_objects: List of S3 objects to sign\n#     base_url: Optional base URL for the presigned URLs (defaults to WM_BASE_URL)\n#     expiry_secs: How long the signatures stay valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     List of signed public URLs\n# \n# Example:\n#     >>> s3_objs = [S3Object(s3=\"/path/to/file1.txt\"), S3Object(s3=\"/path/to/file2.txt\")]\n#     >>> urls = client.get_presigned_s3_public_urls(s3_objs)\ndef get_presigned_s3_public_urls(s3_objects: list[S3Object | str], base_url: str | None = None, expiry_secs: int | None = None) -> list[str]\n\n# Generate a presigned public URL for an S3 object.\n# If the S3 object is not signed yet, it will be signed first.\n# \n# Args:\n#     s3_object: S3 object to sign\n#     base_url: Optional base URL for the presigned URL (defaults to WM_BASE_URL)\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     Signed public URL\n# \n# Example:\n#     >>> s3_obj = S3Object(s3=\"/path/to/file.txt\")\n#     >>> url = client.get_presigned_s3_public_url(s3_obj)\ndef get_presigned_s3_public_url(s3_object: S3Object | str, base_url: str | None = None, expiry_secs: int | None = None) -> str\n\n# Get the current user information.\n# \n# Returns:\n#     User details dictionary\ndef whoami() -> dict\n\n# Get the current user information (alias for whoami).\n# \n# Returns:\n#     User details dictionary\ndef user() -> dict\n\n# Get the state resource path from environment.\n# \n# Returns:\n#     State path string\ndef state_path() -> str\n\n# Get the workflow state.\n# \n# Returns:\n#     State value or None if not set\ndef state() -> Any\n\n# Set the state in the shared folder using pickle\ndef set_shared_state_pickle(value: Any, path: str = 'state.pickle') -> None\n\n# Get the state in the shared folder using pickle\ndef get_shared_state_pickle(path: str = 'state.pickle') -> Any\n\n# Set the state in the shared folder using pickle\ndef set_shared_state(value: Any, path: str = 'state.json') -> None\n\n# Get the state in the shared folder using pickle\ndef get_shared_state(path: str = 'state.json') -> None\n\n# Get URLs needed for resuming a flow after suspension.\n# \n# Args:\n#     approver: Optional approver name\n#     flow_level: If True, generate resume URLs for the parent flow instead of the\n#         specific step. This allows pre-approvals that can be consumed by any later\n#         suspend step in the same flow.\n# \n# Returns:\n#     Dictionary with approvalPage, resume, and cancel URLs\ndef get_resume_urls(approver: str = None, flow_level: bool = None) -> dict\n\n# Get the resume URLs bound to one ``wait_for_approval`` step of this workflow.\n# \n# Args:\n#     step_key: Checkpoint key of the approval step, as passed to\n#         ``wait_for_approval(key=...)``\n#     approver: Optional approver name\n# \n# Returns:\n#     Dictionary with approvalPage, resume, and cancel URLs\ndef get_approval_urls(step_key: str = 'approval', approver: str = None) -> dict\n\n# Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n# \n# **[Enterprise Edition Only]** To include form fields in the Slack approval request, use the \"Advanced -> Suspend -> Form\" functionality.\n# Learn more at: https://www.windmill.dev/docs/flows/flow_approval#form\n# \n# :param slack_resource_path: The path to the Slack resource in Windmill.\n# :type slack_resource_path: str\n# :param channel_id: The Slack channel ID where the approval request will be sent.\n# :type channel_id: str\n# :param message: Optional custom message to include in the Slack approval request.\n# :type message: str, optional\n# :param approver: Optional user ID or name of the approver for the request.\n# :type approver: str, optional\n# :param default_args_json: Optional dictionary defining or overriding the default arguments for form fields.\n# :type default_args_json: dict, optional\n# :param dynamic_enums_json: Optional dictionary overriding the enum default values of enum form fields.\n# :type dynamic_enums_json: dict, optional\n# \n# :raises Exception: If the function is not called within a flow or flow preview.\n# :raises Exception: If the required flow job or flow step environment variables are not set.\n# \n# :return: None\n# \n# **Usage Example:**\n#     >>> client.request_interactive_slack_approval(\n#     ...     slack_resource_path=\"/u/alex/my_slack_resource\",\n#     ...     channel_id=\"admins-slack-channel\",\n#     ...     message=\"Please approve this request\",\n#     ...     approver=\"approver123\",\n#     ...     default_args_json={\"key1\": \"value1\", \"key2\": 42},\n#     ...     dynamic_enums_json={\"foo\": [\"choice1\", \"choice2\"], \"bar\": [\"optionA\", \"optionB\"]},\n#     ... )\n# \n# **Notes:**\n# - This function must be executed within a Windmill flow or flow preview.\n# - The function checks for required environment variables (`WM_FLOW_JOB_ID`, `WM_FLOW_STEP_ID`) to ensure it is run in the appropriate context.\ndef request_interactive_slack_approval(slack_resource_path: str, channel_id: str, message: str = None, approver: str = None, default_args_json: dict = None, dynamic_enums_json: dict = None) -> None\n\n# Send a message to a Microsoft Teams conversation with conversation_id, where success is used to style the message\ndef send_teams_message(conversation_id: str, text: str, success: bool = True, card_block: dict = None)\n\n# Get a DataTable client for SQL queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n#     role: Connect as this data table role instead of the data table's default one.\n#         Only meaningful on a data table under roles, and only for a role you are a\n#         tenant of.\n# \n# Returns:\n#     DataTableClient instance\ndef datatable(name: str = 'main', *, role: Optional[str] = None)\n\n# Get a DuckLake client for DuckDB queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n# \n# Returns:\n#     DucklakeClient instance\ndef ducklake(name: str = 'main')\n\ndef init_global_client(f)\n\ndef deprecate(in_favor_of: str)\n\n# Get the current workspace ID.\n# \n# Returns:\n#     Workspace ID string\ndef get_workspace() -> str\n\ndef get_version() -> str\n\n# Create a script job and return its job ID.\n# \n# Args:\n#     hash_or_path: Script hash or path (determined by presence of '/')\n#     args: Script arguments\n#     scheduled_in_secs: Delay before execution in seconds\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Job ID string\ndef run_script_async(hash_or_path: str, args: Dict[str, Any] = None, scheduled_in_secs: int = None, tag: str = None) -> str\n\n# Run a script synchronously by hash and return its result.\n# \n# Args:\n#     hash: Script hash\n#     args: Script arguments\n#     verbose: Enable verbose logging\n#     assert_result_is_not_none: Raise exception if result is None\n#     cleanup: Register cleanup handler to cancel job on exit\n#     timeout: Maximum time to wait\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Script result\ndef run_script_sync(hash: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any\n\n# Run a script synchronously by path and return its result.\n# \n# Args:\n#     path: Script path\n#     args: Script arguments\n#     verbose: Enable verbose logging\n#     assert_result_is_not_none: Raise exception if result is None\n#     cleanup: Register cleanup handler to cancel job on exit\n#     timeout: Maximum time to wait\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Script result\ndef run_script_by_path_sync(path: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from DuckDB\ndef duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from Polars\ndef polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection using boto3\ndef boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings\n\n# Get the state resource path from environment.\n# \n# Returns:\n#     State path string\ndef get_state_path() -> str\n\n# Parse resource syntax from string.\ndef parse_resource_syntax(s: str) -> Optional[str]\n\n# Parse S3 object from a `s3://<storage>/<key>` URI string (`s3:///<key>`\n# for the default storage) or S3Object format. Any other string raises\n# rather than falling back to an auto-generated key: an auto key is\n# requested by omitting the object, and a fallback would silently misplace\n# the upload on any typo.\ndef parse_s3_object(s3_object: S3Object | str) -> S3Object\n\n# Parse variable syntax from string.\ndef parse_variable_syntax(s: str) -> Optional[str]\n\n# Append a text to the result stream.\n# \n# Args:\n#     text: text to append to the result stream\ndef append_to_result_stream(text: str) -> None\n\n# Stream to the result stream.\n# \n# Args:\n#     stream: stream to stream to the result stream\ndef stream_result(stream) -> None\n\n# Execute a SQL query against the DataTable.\n# \n# Args:\n#     sql: SQL query string with $1, $2, etc. placeholders\n#     *args: Positional arguments to bind to query placeholders\n# \n# Returns:\n#     SqlQuery instance for fetching results\ndef query(sql: str, *args) -> SqlQuery\n\n# Idempotently materialize the rows of `select_sql` into ducklake\n# `table` for one `partition` (or the whole table when `partition` is\n# None). Client-side equivalent of the `// materialize` engine: with\n# `unique_key` it upserts within the slice (delete-by-key + insert);\n# without it, it replaces (whole table → CREATE OR REPLACE; partition →\n# delete the partition + insert). Re-running the same slice is safe — the\n# backfill / failure-recovery contract.\n# \n# The partition value is bound as a DuckDB arg (never string-interpolated)\n# so it cannot inject SQL. `select_sql` is trusted (your own query).\ndef upsert_partition(table: str, select_sql: str, partition: str = None, unique_key: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# INSERT-only materialization (no dedup / no replace) for an immutable\n# event-log table — for one `partition`, or the whole table when\n# `partition` is None. NOTE: unlike `upsert_partition`, re-running the same\n# slice duplicates rows — use only for append-only sources.\ndef append_partition(table: str, select_sql: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# Read a materialized ducklake table, optionally a single partition.\ndef read(table: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# Execute query and fetch results.\n# \n# Args:\n#     result_collection: Optional result collection mode\n# \n# Returns:\n#     Query results\ndef fetch(result_collection: str | None = None)\n\n# Execute query and fetch first row of results.\n# \n# Returns:\n#     First row of query results\ndef fetch_one()\n\n# Execute query and fetch first row of results. Return result as a scalar value.\n# \n# Returns:\n#     First row of query result as a scalar value\ndef fetch_one_scalar()\n\n# Execute query and don't return any results.\n#         \ndef execute()\n\n# DuckDB executor requires explicit argument types at declaration\n# These types exist in both DuckDB and Postgres\n# Check that the types exist if you plan to extend this function for other SQL engines.\ndef infer_sql_type(value) -> str\n\ndef parse_sql_client_name(name: str) -> tuple[str, Optional[str]]\n\n# Decorator that marks a function as a workflow task.\n# \n# Works in both WAC v1 (sync, HTTP-based dispatch) and WAC v2\n# (async, checkpoint/replay) modes:\n# \n# - **v2 (inside @workflow)**: dispatches as a checkpoint step.\n# - **v1 (WM_JOB_ID set, no @workflow)**: dispatches via HTTP API.\n# - **Standalone, or called from inside another v2 task**: executes the\n#   function body directly. A task runs as a single job, so the tasks it\n#   calls run in that same job, without their own options.\n# \n# A task runs as its own job, so its result is always encoded as JSON and\n# decoded back before the caller sees it: a ``datetime`` comes back as a\n# string, a tuple as a list.\n# \n# ``retry`` re-dispatches the task after a failure, inside ``@workflow`` only.\n# Every attempt is a step of its own (``call_api``, ``call_api#2``, ...) and\n# the wait between two of them is a durable sleep, so a retrying task holds no\n# worker while it backs off. Keys: ``attempts`` (retries after the first\n# failure, a whole number from 0 to 100), ``delay`` (seconds before the first\n# retry, sub-second delays dropped), ``multiplier`` (applied to the delay\n# after each attempt, 1 keeps it constant), ``max_delay`` (ceiling in\n# seconds). ``attempts`` is required, and an out-of-range or unknown key is\n# rejected where the policy is written.\n# \n# A workflow sleeps once per round, so tasks backing off in the same fan-out\n# wait one after another rather than together: the delay before a fan-out\n# retries is the sum of every backoff pending in it, not the longest one, and\n# it grows with both the width of the fan-out and ``attempts``. Retries with\n# no ``delay`` all go out in a single round.\n# \n# ``cache_ttl`` serves a previous result of the task for that many seconds\n# instead of running it again. A task is keyed on its step key (its name and\n# call order) and the workflow's input, not on the arguments it is called\n# with, so cache one only when whether it runs, and what it receives, follow\n# from the workflow's input alone. A ``task_script`` target is keyed on the\n# arguments it is called with. It has no effect on a ``task_flow`` target,\n# which keeps its flow's own cache policy.\n# \n# Usage::\n# \n#     @task\n#     async def extract_data(url: str): ...\n# \n#     @task(path=\"f/external_script\", timeout=600, tag=\"gpu\")\n#     async def run_external(x: int): ...\n# \n#     @task(retry={\"attempts\": 3, \"delay\": 30, \"multiplier\": 2})\n#     async def call_api(payload: dict): ...\ndef task(_func = None, *, path: Optional[str] = None, tag: Optional[str] = None, timeout: Optional[int] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Create a task that dispatches to a separate Windmill script.\n# \n# ``retry`` takes the same policy as :func:`task`.\n# \n# Usage::\n# \n#     extract = task_script(\"f/data/extract\", timeout=600)\n# \n#     @workflow\n#     async def main():\n#         data = await extract(url=\"https://...\")\ndef task_script(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Create a task that dispatches to a separate Windmill flow.\n# \n# ``retry`` takes the same policy as :func:`task`.\n# \n# Usage::\n# \n#     pipeline = task_flow(\"f/etl/pipeline\", priority=10)\n# \n#     @workflow\n#     async def main():\n#         result = await pipeline(input=data)\ndef task_flow(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Decorator marking an async function as a workflow-as-code entry point.\n# \n# The function must be **deterministic**: given the same inputs it must call\n# tasks in the same order on every replay. Branching on task results is fine\n# (results are replayed from checkpoint), but branching on external state\n# (current time, random values, external API calls) must use ``step()`` to\n# checkpoint the value so replays see the same result.\ndef workflow(func)\n\n# Execute ``fn`` inline and checkpoint the result.\n# \n# On replay the cached value is returned without re-executing ``fn``.\n# Use for lightweight deterministic operations (timestamps, random IDs,\n# config reads) that should not incur the overhead of a child job.\n# \n# ``fn``'s result is encoded as JSON and decoded back before it is returned,\n# so the round that runs the body sees the same types every replay sees:\n# a ``datetime`` comes back as a string, a tuple as a list.\n# \n# Called from inside a task body, ``fn`` runs in place and nothing is\n# checkpointed: a task is a single job and is never replayed.\nasync def step(name: str, fn)\n\n# Server-side sleep — suspend the workflow for the given duration without holding a worker.\n# \n# Inside a @workflow, the parent job suspends and auto-resumes after ``seconds``.\n# Outside a workflow, or inside a task body, falls back to ``asyncio.sleep``.\nasync def sleep(seconds: int)\n\n# Suspend the workflow and wait for an external approval.\n# \n# Pass ``key`` to name the step, then ``get_approval_urls(key)`` yields the URLs\n# that resume exactly this approval — route them through your own channel.\n# Without a key the steps are named ``approval``, ``approval_2``, ...\n# \n# Returns a dict with ``value`` (form data), ``approver``, and ``approved``.\n# \n# Args:\n#     timeout: Approval timeout in seconds (default 1800).\n#     form: Optional form schema for the approval page.\n#     self_approval: Whether the user who triggered the flow can approve it (default True).\n#     key: Optional checkpoint key naming this approval step.\n#     skin: ``\"minimal\"`` shows approvers only the request (form and approve/reject)\n#         instead of the detailed page with the workflow's details.\n#     description: Shown to approvers above the form: a string, or a rich value such as\n#         ``{\"markdown\": \"...\"}``.\n# \n# Example::\n# \n#     urls = await step(\"urls\", lambda: get_approval_urls(\"manager\"))\n#     await step(\"notify\", lambda: send_email(urls[\"resume\"], urls[\"cancel\"]))\n#     result = await wait_for_approval(key=\"manager\", timeout=3600)\nasync def wait_for_approval(timeout: int = 1800, form: dict | None = None, self_approval: bool = True, key: str | None = None, skin: Literal['detailed', 'minimal'] | None = None, description: str | dict | None = None) -> dict\n\n# Process items in parallel with optional concurrency control.\n# \n# Each item is processed by calling ``fn(item)``, which should be a @task.\n# Items are dispatched in batches of ``concurrency`` (default: all at once).\n# \n# Example::\n# \n#     @task\n#     async def process(item: str):\n#         ...\n# \n#     results = await parallel(items, process, concurrency=5)\nasync def parallel(items, fn, *, concurrency: Optional[int] = None)\n\n# Commit Kafka offsets for a trigger with auto_commit disabled.\n# \n# Args:\n#     trigger_path: Path to the Kafka trigger (from event['wm_trigger']['trigger_path'])\n#     topic: Kafka topic name (from event['topic'])\n#     partition: Partition number (from event['partition'])\n#     offset: Message offset to commit (from event['offset'])\ndef commit_kafka_offsets(trigger_path: str, topic: str, partition: int, offset: int) -> None\n\n",
		"script.deno": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (deno)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# TypeScript (Deno)\n\nDeno runtime with npm support via `npm:` prefix and native Deno libraries.\n\n**Prefer Bun (`write-script-bun`) for TypeScript.** Only use Deno when the script specifically requires the Deno runtime — Deno's standard library or `deno.land` URL imports that have no npm equivalent. For all other TypeScript, use Bun instead.\n\n## Structure\n\nExport a single **async** function called `main`:\n\n```typescript\nexport async function main(param1: string, param2: number) {\n  // Your code here\n  return { result: param1, count: param2 };\n}\n```\n\nDo not call the main function. Libraries are installed automatically.\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nUse the `RT` namespace for resource types:\n\n```typescript\nexport async function main(stripe: RT.Stripe) {\n  // stripe contains API key and config from the resource\n}\n```\n\nOnly use resource types if you need them to satisfy the instructions. Always use the RT namespace.\n\n## Imports\n\n```typescript\n// npm packages use npm: prefix\nimport Stripe from \"npm:stripe\";\nimport { someFunction } from \"npm:some-package\";\n\n// Deno standard library\nimport { serve } from \"https://deno.land/std/http/server.ts\";\n```\n\n## Windmill Client\n\nImport the windmill client for platform interactions:\n\n```typescript\nimport * as wmill from \"windmill-client\";\n```\n\n**Prefer `windmill-client` over raw `fetch` for anything that talks to Windmill** — reading resources/variables/states, running scripts and flows, S3 object operations, etc. It handles auth, the workspace, and the base URL for you. Reserve `fetch` for calling *external* HTTP APIs that aren't Windmill.\n\nThe full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method instead of guessing or falling back to `fetch`.\n\n## Preprocessor Scripts\n\nFor preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:\n\n```typescript\ntype Event = {\n  kind:\n    | \"webhook\"\n    | \"http\"\n    | \"websocket\"\n    | \"kafka\"\n    | \"email\"\n    | \"nats\"\n    | \"postgres\"\n    | \"sqs\"\n    | \"mqtt\"\n    | \"amqp\"\n    | \"gcp\"\n    | \"azure\";\n  body: any;\n  headers: Record<string, string>;\n  query: Record<string, string>;\n};\n\nexport async function preprocessor(event: Event) {\n  return {\n    param1: event.body.field1,\n    param2: event.query.id,\n  };\n}\n```\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.\n\n### Receiving an S3Object as a script parameter\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\nexport async function main(file: wmill.S3Object) {\n  const content = await wmill.loadS3File(file);\n  // ...\n}\n```\n\n### S3 operations\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\n// Load file content from S3\nconst content: Uint8Array = await wmill.loadS3File(s3object);\n\n// Load file as stream\nconst blob: Blob = await wmill.loadS3FileStream(s3object);\n\n// Write file to S3\nconst result: wmill.S3Object = await wmill.writeS3File(\n  s3object, // Target path (or undefined to auto-generate)\n  fileContent, // string or Blob\n  s3ResourcePath // Optional: specific S3 resource to use\n);\n```\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"script.go": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (go)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Go\n\n## Structure\n\nThe file package must be `inner` and export a function called `main`:\n\n```go\npackage inner\n\nfunc main(param1 string, param2 int) (map[string]interface{}, error) {\n    return map[string]interface{}{\n        \"result\": param1,\n        \"count\":  param2,\n    }, nil\n}\n```\n\n**Important:**\n- Package must be `inner`\n- Return type must be `({return_type}, error)`\n- Function name is `main` (lowercase)\n\n## Return Types\n\nThe return type can be any Go type that can be serialized to JSON:\n\n```go\npackage inner\n\ntype Result struct {\n    Name  string `json:\"name\"`\n    Count int    `json:\"count\"`\n}\n\nfunc main(name string, count int) (Result, error) {\n    return Result{\n        Name:  name,\n        Count: count,\n    }, nil\n}\n```\n\n## Error Handling\n\nReturn errors as the second return value:\n\n```go\npackage inner\n\nimport \"errors\"\n\nfunc main(value int) (string, error) {\n    if value < 0 {\n        return \"\", errors.New(\"value must be positive\")\n    }\n    return \"success\", nil\n}\n```\n",
		"script.bash": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (bash)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Bash\n\n## Structure\n\nDo not include `#!/bin/bash`. Arguments are obtained as positional parameters:\n\n```bash\n# Get arguments\nvar1=\"$1\"\nvar2=\"$2\"\n\necho \"Processing $var1 and $var2\"\n\n# Return JSON by echoing to stdout\necho \"{\\\"result\\\": \\\"$var1\\\", \\\"count\\\": $var2}\"\n```\n\n**Important:**\n- Do not include shebang (`#!/bin/bash`)\n- Arguments are always strings\n- Access with `$1`, `$2`, etc.\n\n## Output\n\nThe script output is captured as the result. For structured data, output valid JSON:\n\n```bash\nname=\"$1\"\ncount=\"$2\"\n\n# Output JSON result\ncat << EOF\n{\n  \"name\": \"$name\",\n  \"count\": $count,\n  \"timestamp\": \"$(date -Iseconds)\"\n}\nEOF\n```\n\n## Environment Variables\n\nEnvironment variables set in Windmill are available:\n\n```bash\n# Access environment variable\necho \"Workspace: $WM_WORKSPACE\"\necho \"Job ID: $WM_JOB_ID\"\n```\n",
		"script.powershell": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (powershell)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# PowerShell\n\n## Structure\n\nArguments are obtained by calling the `param` function on the first line:\n\n```powershell\nparam($Name, $Count = 0, [int]$Age)\n\n# Your code here\nWrite-Output \"Processing $Name, count: $Count, age: $Age\"\n\n# Return object\n@{\n    name = $Name\n    count = $Count\n    age = $Age\n}\n```\n\n## Parameter Types\n\nYou can specify types for parameters:\n\n```powershell\nparam(\n    [string]$Name,\n    [int]$Count = 0,\n    [bool]$Enabled = $true,\n    [array]$Items\n)\n\n@{\n    name = $Name\n    count = $Count\n    enabled = $Enabled\n    items = $Items\n}\n```\n\n## Return Values\n\nReturn values by outputting them at the end of the script:\n\n```powershell\nparam($Input)\n\n$result = @{\n    processed = $true\n    data = $Input\n    timestamp = Get-Date -Format \"o\"\n}\n\n$result\n```\n",
		"script.postgresql": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (postgresql)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# PostgreSQL\n\nArguments are obtained directly in the statement with `$1::{type}`, `$2::{type}`, etc.\n\nName the parameters by adding comments at the beginning of the script (without specifying the type):\n\n```sql\n-- $1 name1\n-- $2 name2 = default_value\nSELECT * FROM users WHERE name = $1::TEXT AND age > $2::INT;\n```\n\n## Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for\nit, downloads the file, and binds it as a `jsonb` parameter — Parquet/CSV files\nare decoded server-side into a JSON array of records, JSON/JSONL pass through.\nConsume with `jsonb_to_recordset` (or any `jsonb` API):\n\n```sql\n-- $1 file (s3object)\nSELECT *\nFROM jsonb_to_recordset($1::jsonb) AS r(id INT, name TEXT);\n```\n\n## Streaming query results to S3\n\nAdd a `-- s3` directive at the top of the script to stream the result set to S3\ninstead of returning rows. Windmill writes the file and returns its `S3Object`\nas the script result.\n\n```sql\n-- s3 prefix=exports/users format=parquet\nSELECT id, name FROM users;\n```\n\nAll keys are optional: `prefix` (object key prefix), `storage` (named storage —\nomit to use the workspace default), `format` (`json` (default), `parquet`, or\n`csv`). Use this for large result sets — rows stream directly to S3 instead of\nbeing buffered as the script return value.\n",
		"script.mysql": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (mysql)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# MySQL\n\nArguments use `?` placeholders.\n\nName the parameters by adding comments before the statement:\n\n```sql\n-- ? name1 (text)\n-- ? name2 (int) = 0\nSELECT * FROM users WHERE name = ? AND age > ?;\n```\n\n## Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for\nit, downloads the file, and binds it as JSON text — Parquet/CSV files are\ndecoded server-side into a JSON array of records, JSON/JSONL pass through.\nConsume with `JSON_TABLE`:\n\n```sql\n-- ? file (s3object)\nSELECT id, name\nFROM JSON_TABLE(?, '$[*]'\n  COLUMNS (id INT PATH '$.id', name VARCHAR(200) PATH '$.name')\n) AS r;\n```\n\n## Streaming query results to S3\n\nAdd a `-- s3` directive at the top of the script to stream the result set to S3\ninstead of returning rows. Windmill writes the file and returns its `S3Object`\nas the script result.\n\n```sql\n-- s3 prefix=exports/users format=parquet\nSELECT id, name FROM users;\n```\n\nAll keys are optional: `prefix` (object key prefix), `storage` (named storage —\nomit to use the workspace default), `format` (`json` (default), `parquet`, or\n`csv`). Use this for large result sets — rows stream directly to S3 instead of\nbeing buffered as the script return value.\n",
		"script.bigquery": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (bigquery)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# BigQuery\n\nArguments use `@name` syntax.\n\nName the parameters by adding comments before the statement:\n\n```sql\n-- @name1 (string)\n-- @name2 (int64) = 0\nSELECT * FROM users WHERE name = @name1 AND age > @name2;\n```\n\n## Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for\nit, downloads the file, and binds it as a `STRING` JSON parameter — Parquet/CSV\nfiles are decoded server-side into a JSON array of records, JSON/JSONL pass\nthrough. Consume with `JSON_EXTRACT_ARRAY` / `JSON_VALUE`:\n\n```sql\n-- @file (s3object)\nSELECT\n  CAST(JSON_VALUE(row, '$.id') AS INT64) AS id,\n  JSON_VALUE(row, '$.name') AS name\nFROM UNNEST(JSON_EXTRACT_ARRAY(@file)) AS row;\n```\n\n## Streaming query results to S3\n\nAdd a `-- s3` directive at the top of the script to stream the result set to S3\ninstead of returning rows. Windmill writes the file and returns its `S3Object`\nas the script result.\n\n```sql\n-- s3 prefix=exports/users format=parquet\nSELECT id, name FROM users;\n```\n\nAll keys are optional: `prefix` (object key prefix), `storage` (named storage —\nomit to use the workspace default), `format` (`json` (default), `parquet`, or\n`csv`). Use this for large result sets — rows stream directly to S3 instead of\nbeing buffered, bypassing the 10000-row return cap.\n",
		"script.snowflake": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (snowflake)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Snowflake\n\nArguments use `?` placeholders.\n\nName the parameters by adding comments before the statement:\n\n```sql\n-- ? name1 (text)\n-- ? name2 (number) = 0\nSELECT * FROM users WHERE name = ? AND age > ?;\n```\n\n## Array parameters\n\nDeclare a list with a `[]` suffix on the element type (`(varchar[])`,\n`(int[])`), or `(array)` for an untyped one. Snowflake cannot bind an array\ndirectly, so Windmill binds the list as JSON text: wrap the placeholder with\n`PARSE_JSON(?)`, and cast to `ARRAY` where a function expects one.\n\n```sql\n-- ? groups (varchar[])\nSELECT o.* FROM orders o\nWHERE ARRAY_CONTAINS(o.owner_group::VARIANT, PARSE_JSON(?)::ARRAY);\n```\n\nOr join on the elements with `FLATTEN`:\n\n```sql\n-- ? groups (varchar[])\nSELECT o.* FROM orders o\nJOIN TABLE(FLATTEN(input => PARSE_JSON(?))) g ON g.value::STRING = o.owner_group;\n```\n\n## Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for\nit, downloads the file, and binds it as JSON text — Parquet/CSV files are\ndecoded server-side into a JSON array of records, JSON/JSONL pass through.\nWrap the bind with `PARSE_JSON(?)` and walk it with `LATERAL FLATTEN`:\n\n```sql\n-- ? file (s3object)\nSELECT\n  v.value:id::NUMBER AS id,\n  v.value:name::STRING AS name\nFROM LATERAL FLATTEN(input => PARSE_JSON(?)) v;\n```\n\n## Streaming query results to S3\n\nAdd a `-- s3` directive at the top of the script to stream the result set to S3\ninstead of returning rows. Windmill writes the file and returns its `S3Object`\nas the script result.\n\n```sql\n-- s3 prefix=exports/users format=parquet\nSELECT id, name FROM users;\n```\n\nAll keys are optional: `prefix` (object key prefix), `storage` (named storage —\nomit to use the workspace default), `format` (`json` (default), `parquet`, or\n`csv`). Use this for large result sets — rows stream directly to S3 instead of\nbeing buffered, bypassing the 10000-row return cap.\n",
		"script.mssql": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (mssql)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Microsoft SQL Server (MSSQL)\n\nArguments use `@P1`, `@P2`, etc.\n\nName the parameters by adding comments before the statement:\n\n```sql\n-- @P1 name1 (varchar)\n-- @P2 name2 (int) = 0\nSELECT * FROM users WHERE name = @P1 AND age > @P2;\n```\n\n## Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for\nit, downloads the file, and binds it as `nvarchar(max)` JSON text — Parquet/CSV\nfiles are decoded server-side into a JSON array of records, JSON/JSONL pass\nthrough. Consume with `OPENJSON`:\n\n```sql\n-- @P1 file (s3object)\nSELECT id, name\nFROM OPENJSON(@P1)\nWITH (id INT, name NVARCHAR(200));\n```\n\n## Streaming query results to S3\n\nAdd a `-- s3` directive at the top of the script to stream the result set to S3\ninstead of returning rows. Windmill writes the file and returns its `S3Object`\nas the script result.\n\n```sql\n-- s3 prefix=exports/users format=parquet\nSELECT id, name FROM users;\n```\n\nAll keys are optional: `prefix` (object key prefix), `storage` (named storage —\nomit to use the workspace default), `format` (`json` (default), `parquet`, or\n`csv`). Use this for large result sets — rows stream directly to S3 instead of\nbeing buffered as the script return value.\n",
		"script.oracledb": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (oracledb)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n",
		"script.graphql": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (graphql)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# GraphQL\n\n## Structure\n\nWrite GraphQL queries or mutations. Arguments can be added as query parameters:\n\n```graphql\nquery GetUser($id: ID!) {\n  user(id: $id) {\n    id\n    name\n    email\n  }\n}\n```\n\n## Variables\n\nVariables are passed as script arguments and automatically bound to the query:\n\n```graphql\nquery SearchProducts($query: String!, $limit: Int = 10) {\n  products(search: $query, first: $limit) {\n    edges {\n      node {\n        id\n        name\n        price\n      }\n    }\n  }\n}\n```\n\n## Mutations\n\n```graphql\nmutation CreateUser($input: CreateUserInput!) {\n  createUser(input: $input) {\n    id\n    name\n    createdAt\n  }\n}\n```\n",
		"script.nativets": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (nativets)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"script.bun": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (bun)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# TypeScript (Bun)\n\nBun runtime with full npm ecosystem and fastest execution. **Bun is the default and preferred TypeScript runtime** — choose it for any TypeScript script unless there is a major reason to use Deno for that specific use-case.\n\n## Structure\n\nExport a single **async** function called `main`:\n\n```typescript\nexport async function main(param1: string, param2: number) {\n  // Your code here\n  return { result: param1, count: param2 };\n}\n```\n\nDo not call the main function. Libraries are installed automatically.\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nUse the `RT` namespace for resource types:\n\n```typescript\nexport async function main(stripe: RT.Stripe) {\n  // stripe contains API key and config from the resource\n}\n```\n\nOnly use resource types if you need them to satisfy the instructions. Always use the RT namespace.\n\n## Imports\n\n```typescript\nimport Stripe from \"stripe\";\nimport { someFunction } from \"some-package\";\n```\n\n## Prefer `//native` when the runtime allows it\n\nIf a script only needs `fetch` and the JavaScript standard library — including when it uses `windmill-client` — prefer making it a **native** script: add `//native` as the first line and write it with the `write-script-bunnative` skill. Native scripts run on a lightweight V8 isolate, start faster, and parallelize heavily. `windmill-client` works on the native worker (its calls go over `fetch`), so needing the Windmill client is **not** a reason to avoid `//native`. Use the regular `bun` language only when the code (or a dependency) needs Node/Bun runtime APIs — `node:*` modules, the filesystem, child processes, or native addons.\n\n## Windmill Client\n\nImport the windmill client for platform interactions:\n\n```typescript\nimport * as wmill from \"windmill-client\";\n```\n\n**Prefer `windmill-client` over raw `fetch` for anything that talks to Windmill** — reading resources/variables/states, running scripts and flows, S3 object operations, etc. It handles auth, the workspace, and the base URL for you, so you don't hand-roll URLs or tokens. Reserve `fetch` for calling *external* HTTP APIs that aren't Windmill.\n\nThe full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method to use instead of guessing or falling back to `fetch`.\n\n## Preprocessor Scripts\n\nFor preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:\n\n```typescript\ntype Event = {\n  kind:\n    | \"webhook\"\n    | \"http\"\n    | \"websocket\"\n    | \"kafka\"\n    | \"email\"\n    | \"nats\"\n    | \"postgres\"\n    | \"sqs\"\n    | \"mqtt\"\n    | \"amqp\"\n    | \"gcp\"\n    | \"azure\";\n  body: any;\n  headers: Record<string, string>;\n  query: Record<string, string>;\n};\n\nexport async function preprocessor(event: Event) {\n  return {\n    param1: event.body.field1,\n    param2: event.query.id,\n  };\n}\n```\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.\n\n### Receiving an S3Object as a script parameter\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\nexport async function main(file: wmill.S3Object) {\n  const content = await wmill.loadS3File(file);\n  // ...\n}\n```\n\n### S3 operations\n\n```typescript\nimport * as wmill from \"windmill-client\";\n\n// Load file content from S3\nconst content: Uint8Array = await wmill.loadS3File(s3object);\n\n// Load file as stream\nconst blob: Blob = await wmill.loadS3FileStream(s3object);\n\n// Write file to S3\nconst result: wmill.S3Object = await wmill.writeS3File(\n  s3object, // Target path (or undefined to auto-generate)\n  fileContent, // string or Blob\n  s3ResourcePath // Optional: specific S3 resource to use\n);\n```\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"script.php": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (php)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# PHP\n\n## Structure\n\nThe script must start with `<?php` and contain at least one function called `main`:\n\n```php\n<?php\n\nfunction main(string $param1, int $param2) {\n    return [\"result\" => $param1, \"count\" => $param2];\n}\n```\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nYou need to **redefine** the type of the resources that are needed before the main function. Always check if the class already exists using `class_exists`:\n\n```php\n<?php\n\nif (!class_exists('Postgresql')) {\n    class Postgresql {\n        public string $host;\n        public int $port;\n        public string $user;\n        public string $password;\n        public string $dbname;\n    }\n}\n\nfunction main(Postgresql $db) {\n    // $db contains the database connection details\n}\n```\n\nThe resource type name has to be exactly as specified.\n\n## Library Dependencies\n\nSpecify library dependencies as comments before the main function:\n\n```php\n<?php\n\n// require:\n// guzzlehttp/guzzle\n// stripe/stripe-php@^10.0\n\nfunction main() {\n    // Libraries are available\n}\n```\n\nOne dependency per line. No need to require autoload, it is already done.\n",
		"script.rust": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (rust)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Rust\n\n## Structure\n\nThe script must contain a function called `main` with proper return type:\n\n```rust\nuse anyhow::anyhow;\nuse serde::Serialize;\n\n#[derive(Serialize, Debug)]\nstruct ReturnType {\n    result: String,\n    count: i32,\n}\n\nfn main(param1: String, param2: i32) -> anyhow::Result<ReturnType> {\n    Ok(ReturnType {\n        result: param1,\n        count: param2,\n    })\n}\n```\n\n**Important:**\n- Arguments should be owned types\n- Return type must be serializable (`#[derive(Serialize)]`)\n- Return type is `anyhow::Result<T>`\n\n## Dependencies\n\nPackages must be specified with a partial cargo.toml at the beginning of the script:\n\n```rust\n//! ```cargo\n//! [dependencies]\n//! anyhow = \"1.0.86\"\n//! reqwest = { version = \"0.11\", features = [\"json\"] }\n//! tokio = { version = \"1\", features = [\"full\"] }\n//! ```\n\nuse anyhow::anyhow;\n// ... rest of the code\n```\n\n**Note:** Serde is already included, no need to add it again.\n\n## Async Functions\n\nIf you need to handle async functions (e.g., using tokio), keep the main function sync and create the runtime inside:\n\n```rust\n//! ```cargo\n//! [dependencies]\n//! anyhow = \"1.0.86\"\n//! tokio = { version = \"1\", features = [\"full\"] }\n//! reqwest = { version = \"0.11\", features = [\"json\"] }\n//! ```\n\nuse anyhow::anyhow;\nuse serde::Serialize;\n\n#[derive(Serialize, Debug)]\nstruct Response {\n    data: String,\n}\n\nfn main(url: String) -> anyhow::Result<Response> {\n    let rt = tokio::runtime::Runtime::new()?;\n    rt.block_on(async {\n        let resp = reqwest::get(&url).await?.text().await?;\n        Ok(Response { data: resp })\n    })\n}\n```\n",
		"script.ansible": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (ansible)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Ansible\n\nWindmill runs Ansible playbooks with `ansible-playbook`. A script is a single YAML\ndocument made of two parts separated by a `---` line: a Windmill **header** and one or\nmore standard Ansible **plays**.\n\n## Structure\n\n```yaml\n---\n# Windmill header: configures inventories, file resources, arguments and dependencies\nextra_vars:\n  world_qualifier:\n    type: string\ndependencies:\n  galaxy:\n    collections:\n      - name: community.general\n  python:\n    - jmespath\n---\n# Standard Ansible plays\n- name: Echo\n  hosts: 127.0.0.1\n  connection: local\n  tasks:\n    - name: Print debug message\n      debug:\n        msg: \"Hello, {{ world_qualifier }} world!\"\n```\n\n## Header\n\nThe header is **not** standard Ansible — it is parsed by Windmill to build the script's\ninputs and runtime environment. Supported keys:\n\n- `extra_vars`: defines the script arguments. Each entry is passed to the playbook via\n  `--extra-vars` and becomes a Jinja variable usable as `{{ name }}` in the plays. Give\n  each argument a `type` (`string`, `number`, `boolean`, `object`, ...) so Windmill can\n  generate the input form.\n- `inventory`: lists inventories. Use `resource_type: ansible_inventory` (optionally\n  pinned with `resource: u/user/your_resource`) or `resource_type: dynamic_inventory`.\n- `files`: writes Windmill resources/variables to files before the run, e.g.\n  `- resource: u/user/template` with `target: ./config.j2`, or\n  `- variable: u/user/ssh_key` with `target: ./ssh_key` and `mode: '0600'`.\n- `dependencies`: `galaxy` collections/roles (installed with `ansible-galaxy`) and\n  `python` pip packages available to the playbook.\n- `options`: extra `ansible-playbook` flags such as `- verbosity: vvv`.\n- `vault_password`: a Windmill variable path to use as the Ansible Vault password.\n\n## Arguments\n\nReference header `extra_vars` directly as Jinja variables in the plays:\n\n```yaml\nextra_vars:\n  name:\n    type: string\n  count:\n    type: number\n---\n- hosts: localhost\n  tasks:\n    - debug:\n        msg: \"{{ name }} x {{ count }}\"\n```\n\n## Environment variables\n\nWindmill contextual variables are available as environment variables and read with the\n`env` lookup:\n\n```yaml\n- debug:\n    msg: \"Running in workspace {{ lookup('env', 'WM_WORKSPACE') }}\"\n```\n\n## Output\n\nTo return a result, write JSON to a `result.json` file in the job directory:\n\n```yaml\n- hosts: localhost\n  tasks:\n    - name: Write result\n      copy:\n        content: \"{{ { 'ok': true, 'value': 42 } | to_json }}\"\n        dest: result.json\n```\n",
		"script.csharp": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (csharp)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# C#\n\nThe script must contain a public static `Main` method inside a class:\n\n```csharp\npublic class Script\n{\n    public static object Main(string name, int count)\n    {\n        return new { Name = name, Count = count };\n    }\n}\n```\n\n**Important:**\n- Class name is irrelevant\n- Method must be `public static`\n- Return type can be `object` or specific type\n\n## NuGet Packages\n\nAdd packages using the `#r` directive at the top:\n\n```csharp\n#r \"nuget: Newtonsoft.Json, 13.0.3\"\n#r \"nuget: RestSharp, 110.2.0\"\n\nusing Newtonsoft.Json;\nusing RestSharp;\n\npublic class Script\n{\n    public static object Main(string url)\n    {\n        var client = new RestClient(url);\n        var request = new RestRequest();\n        var response = client.Get(request);\n        return JsonConvert.DeserializeObject(response.Content);\n    }\n}\n```\n",
		"script.nu": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (nu)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n",
		"script.java": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (java)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# Java\n\nThe script must contain a Main public class with a `public static main()` method:\n\n```java\npublic class Main {\n    public static Object main(String name, int count) {\n        java.util.Map<String, Object> result = new java.util.HashMap<>();\n        result.put(\"name\", name);\n        result.put(\"count\", count);\n        return result;\n    }\n}\n```\n\n**Important:**\n- Class must be named `Main`\n- Method must be `public static Object main(...)`\n- Return type is `Object` or `void`\n\n## Maven Dependencies\n\nAdd dependencies using comments at the top:\n\n```java\n//requirements:\n//com.google.code.gson:gson:2.10.1\n//org.apache.httpcomponents:httpclient:4.5.14\n\nimport com.google.gson.Gson;\n\npublic class Main {\n    public static Object main(String input) {\n        Gson gson = new Gson();\n        return gson.fromJson(input, Object.class);\n    }\n}\n```\n",
		"script.ruby": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (ruby)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n",
		"script.rlang": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (rlang)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# R\n\n## Structure\n\nDefine a `main` function using `<-` or `=` assignment. Parameters become the script inputs:\n\n```r\nlibrary(dplyr)\nlibrary(jsonlite)\n\nmain <- function(x, name = \"default\", flag = TRUE) {\n    df <- tibble(x = x, name = name)\n    result <- df %>% mutate(greeting = paste(\"Hello\", name))\n    return(toJSON(result, auto_unbox = TRUE))\n}\n```\n\n**Important:**\n- The `main` function is required\n- Use `library()` to load packages — they are resolved and installed automatically\n- `jsonlite` is always available (used internally for argument parsing)\n- Return values must be JSON-serializable\n\n## Parameters\n\nR types map to Windmill types:\n- `numeric` → float/int\n- `character` → string\n- `logical` → bool (use `TRUE`/`FALSE`)\n- `list` → object/dict\n- `NULL` → null\n\nDefault values are inferred from the function signature:\n\n```r\nmain <- function(\n    name,              # required string\n    count = 10,        # optional int, default 10\n    verbose = FALSE    # optional bool, default FALSE\n) {\n    # ...\n}\n```\n\n## Resources and Variables\n\nUse the built-in Windmill helpers (no import needed):\n\n```r\nmain <- function() {\n    # Get a variable\n    api_key <- get_variable(\"f/my_folder/api_key\")\n\n    # Get a resource (returns a list)\n    db <- get_resource(\"f/my_folder/postgres_config\")\n    host <- db$host\n    port <- db$port\n\n    return(list(host = host, port = port))\n}\n```\n\n## Output\n\nReturn any JSON-serializable value from `main`. The return value becomes the step result:\n\n```r\nmain <- function(x) {\n    # Return a scalar\n    return(x + 1)\n\n    # Or a list (becomes JSON object)\n    return(list(result = x + 1, status = \"ok\"))\n}\n```\n\n## Annotations\n\nControl execution behavior with comment annotations:\n\n```r\n#renv_verbose = true        # Show verbose renv output during resolution\n#renv_install_verbose = true # Show verbose output during package installation\n#sandbox = true              # Run in nsjail sandbox (requires nsjail)\n```\n",
		"script.duckdb": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (duckdb)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# DuckDB\n\nArguments are defined with comments and used with `$name` syntax:\n\n```sql\n-- $name (text) = default\n-- $age (integer)\nSELECT * FROM users WHERE name = $name AND age > $age;\n```\n\n## Ducklake Integration\n\nAttach Ducklake for data lake operations:\n\n```sql\n-- Main ducklake\nATTACH 'ducklake' AS dl;\n\n-- Named ducklake\nATTACH 'ducklake://my_lake' AS dl;\n\n-- Then query\nSELECT * FROM dl.schema.table;\n```\n\n## External Database Connections\n\nConnect to external databases using resources:\n\n```sql\nATTACH '$res:path/to/resource' AS db (TYPE postgres);\nSELECT * FROM db.schema.table;\n```\n\n## S3 File Operations\n\nRead files from S3 storage:\n\n```sql\n-- Default storage\nSELECT * FROM read_csv('s3:///path/to/file.csv');\n\n-- Named storage\nSELECT * FROM read_csv('s3://storage_name/path/to/file.csv');\n\n-- Parquet files\nSELECT * FROM read_parquet('s3:///path/to/file.parquet');\n\n-- JSON files\nSELECT * FROM read_json('s3:///path/to/file.json');\n```\n\n### Receiving an S3Object as a script parameter\n\nDeclare the arg with type `(s3object)`. Windmill renders an S3 file picker for it\nand binds the arg as the bare `s3://storage/key` URI, which DuckDB's reader\nfunctions consume directly:\n\n```sql\n-- $file (s3object)\nSELECT * FROM read_parquet($file);\n```\n\nWorks with any DuckDB reader: `read_csv($file)`, `read_json($file)`, etc.\n\n### Writing query results to S3\n\nDuckDB writes to S3 natively via `COPY ... TO`:\n\n```sql\nCOPY (SELECT * FROM users) TO 's3:///exports/users.parquet' (FORMAT PARQUET);\n```\n\nUse this instead of the `-- s3` streaming directive supported by the other SQL\ndialects — that directive is not available in DuckDB.\n",
		"script.bunnative": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (bunnative)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n\n\n# TypeScript (Bun Native)\n\nNative TypeScript execution. Native scripts are Bun scripts that run on the native worker — a lightweight V8 isolate that exposes `fetch` and the JavaScript standard library — and can be heavily parallelized. Every script MUST start with `//native` on its first line so Windmill routes it to the native worker; without it the exact same script runs on the regular Bun worker. You may import npm packages and other Windmill scripts (e.g. `./helper.ts`) — imports are resolved and bundled just like a regular Bun script — as long as everything (your code and its dependencies) relies only on `fetch` and the standard library. Libraries that need Node/Bun runtime APIs (filesystem, `node:*` modules, child processes, native addons) will not work on the native worker; use the regular `bun` language for those.\n\n## Structure\n\nExport a single **async** function called `main`:\n\n```typescript\n//native\nexport async function main(param1: string, param2: number) {\n  // Your code here\n  return { result: param1, count: param2 };\n}\n```\n\nDo not call the main function.\n\n## Resource Types\n\nOn Windmill, credentials and configuration are stored in resources and passed as parameters to main.\n\nUse the `RT` namespace for resource types:\n\n```typescript\n//native\nexport async function main(stripe: RT.Stripe) {\n  // stripe contains API key and config from the resource\n}\n```\n\nOnly use resource types if you need them to satisfy the instructions. Always use the RT namespace.\n\n## Imports\n\n**The constraint is the runtime, not the import list.** You may import npm packages and relative Windmill scripts; they are resolved and bundled exactly like a regular Bun script. But the native worker only provides `fetch` and the JavaScript standard library, so any imported code must work using only those. Anything requiring Node/Bun built-ins (`node:fs`, `child_process`, the `Bun` API, native modules) belongs in a regular `bun` script instead. Use the globally available `fetch` for HTTP:\n\n```typescript\n//native\nexport async function main(url: string) {\n  const response = await fetch(url);\n  return await response.json();\n}\n```\n\n## Pure computation: `//no_network`\n\nA native script that only transforms its inputs can declare `//no_network` in its leading comment block (right after `//native`). The runtime then refuses every connection the script attempts: `fetch` to any host, the Windmill API (so `windmill-client` calls fail too), raw sockets and unix sockets, and `WM_TOKEN` is not set. Without `//native` it means something else: the script must run in an nsjail sandbox (`//sandbox`, or instance-wide sandboxing), which then loses its network, and it fails to run otherwise. Use it only when the script needs no external data beyond its arguments:\n\n```typescript\n//native\n//no_network\nexport async function main(items: { price: number; qty: number }[]) {\n  return items.reduce((sum, i) => sum + i.price * i.qty, 0);\n}\n```\n\n## Windmill Client\n\n`windmill-client` works on the native worker (its calls go over `fetch`), so use it as the **preferred way to talk to Windmill** — reading resources/variables/states, running scripts and flows, and the S3 helpers below (`loadS3File`, `loadS3FileStream`, `writeS3File`, `S3Object`). It handles auth, the workspace, and the base URL for you. Reserve raw `fetch` for calling *external* HTTP APIs that aren't Windmill.\n\nThe full `windmill-client` API reference (every exported function and its signature) is included below — consult it for the exact method instead of hand-rolling a `fetch` against the Windmill API.\n\n## Preprocessor Scripts\n\nFor preprocessor scripts, the function should be named `preprocessor` and receives an `event` parameter:\n\n```typescript\n//native\ntype Event = {\n  kind:\n    | \"webhook\"\n    | \"http\"\n    | \"websocket\"\n    | \"kafka\"\n    | \"email\"\n    | \"nats\"\n    | \"postgres\"\n    | \"sqs\"\n    | \"mqtt\"\n    | \"amqp\"\n    | \"gcp\"\n    | \"azure\";\n  body: any;\n  headers: Record<string, string>;\n  query: Record<string, string>;\n};\n\nexport async function preprocessor(event: Event) {\n  return {\n    param1: event.body.field1,\n    param2: event.query.id,\n  };\n}\n```\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations. The `wmill.S3Object` type covers both the `s3://storage/key` URI form (`s3:///key` for the workspace default storage) and the `{ s3, storage? }` record form — always use it instead of redefining your own.\n\n### Receiving an S3Object as a script parameter\n\n```typescript\n//native\nimport * as wmill from \"windmill-client\";\n\nexport async function main(file: wmill.S3Object) {\n  const content = await wmill.loadS3File(file);\n  // ...\n}\n```\n\n### S3 operations\n\n```typescript\n//native\nimport * as wmill from \"windmill-client\";\n\n// Load file content from S3\nconst content: Uint8Array = await wmill.loadS3File(s3object);\n\n// Load file as stream\nconst blob: Blob = await wmill.loadS3FileStream(s3object);\n\n// Write file to S3\nconst result: wmill.S3Object = await wmill.writeS3File(\n  s3object, // Target path (or undefined to auto-generate)\n  fileContent, // string or Blob\n  s3ResourcePath // Optional: specific S3 resource to use\n);\n```\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"script.dbt": "# Global draft script instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, or generate metadata.\n- A script draft is a workspace item: `{ type: 'script', path, summary?, language, value, isDraft }` where `value` is the source code string.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Preserve the current path/language when modifying unless the user asked to change them.\n- Use `edit_script` for small localized changes (provide `old_string`/`new_string`); use `write_script` for full rewrites.\n\n# Windmill script authoring reference (dbt)\n\n# Windmill Script Writing Guide\n\n## General Principles\n\n- A script's inputs are its parameters. Credentials and configuration come in as resource-typed parameters, never hard-coded or read from the environment; the language section below shows how that language declares parameters\n- When no resource type exists for a service, still take its connection as one resource-typed parameter, of a custom type holding the connection fields (base URL, account identifiers, credentials), its secret in a variable the resource references. A custom type is named `c_<name>`, which keeps it apart from a hub type synced later: the parameter takes `RT.C<Name>` in TypeScript, a `c_<name>` TypedDict in Python. A plain string parameter or a variable read inside the code only when the user asks for it: the resource keeps a connection's fields together, lets whoever runs the script pick an existing connection, and keeps the secret out of the arguments a run records. If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services. Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it\n- A Windmill Hub script takes a resource type the hub publishes, so on an instance that has not synced the hub's types that type is missing: say so, and that a superadmin can sync them, before asking for a resource of it. Hub scripts often declare the type inline (`type Stripe = { token: string }`), which is still the `stripe` resource type; in code you write, name it the way the language section shows (`RT.Stripe` in TypeScript) rather than copying the inline declaration\n- Libraries are installed automatically - do not show installation instructions\n- In a language with an entrypoint function (TypeScript, Python, Go, Rust, PHP, R, …), name it `main` (`Main` in C#) and do not call it; in TypeScript it must be async. SQL, GraphQL, Bash, PowerShell and Ansible scripts have no `main`: their language section shows how they take arguments\n- Where the language has a Windmill client (`wmill`), use it to interact with the platform\n- A script's input schema may carry a top-level `prompt_for_ai` string: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that script, and keep it when you rewrite the schema\n\n## Return Values\n\n- A script can return any JSON-serializable value; a SQL script returns the rows its query produces\n- Return values become available to subsequent flow steps via `results.step_id`\n\n## Preprocessor Scripts\n\nPreprocessor scripts process raw trigger data from various sources (webhook, custom HTTP route, SQS, WebSocket, Kafka, NATS, MQTT, AMQP, Postgres, GCP Pub/Sub, Azure, or email) before passing it to the flow. This separates the trigger logic from the flow logic and keeps the auto-generated UI clean.\n\nA preprocessor is written in TypeScript or Python: its function is named `preprocessor` instead of `main`, and it receives a single parameter called `event` (the language section gives its type).\n\nThe returned object determines the parameter values passed to the flow.\ne.g., `{ b: 1, a: 2 }` calls the flow with `a = 2` and `b = 1`, assuming the flow has two inputs called `a` and `b`.\n",
		"flow": "# Global draft flow instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, scaffold local files, or generate metadata.\n- Paths follow the conventions in the system prompt: default to `u/<current-user>/<name>` when the user gave a bare name; only use `f/<folder>/<name>` when the folder is known to exist. Never invent a folder.\n- `write_flow` mirrors flow mode's `set_flow_json`: pass `path`, optional `summary`, optional `description`, required `modules`, and optional `schema`, `preprocessor_module`, `failure_module`, `groups`, and `notes`. `summary` and `description` are top-level flow metadata (not part of the compact value `patch_flow_json` edits); the flow-structure arguments are JSON strings, matching the tool schema descriptions. When overwriting an existing flow, top-level flow settings (see below) are preserved from the current flow — use `patch_flow_json` to change them.\n- `read_workspace_item` returns a compact flow `value` object with `modules`, `schema`, `preprocessor_module`, `failure_module`, `groups`, `notes`, and any top-level flow settings that are set.\n- Top-level flow settings appear as top-level keys of the compact flow value and can be added, edited, or removed with `patch_flow_json`: same_worker, preserve_step_tags, concurrent_limit, concurrency_key, concurrency_time_window_s, debounce_delay_s, debounce_key, debounce_args_to_accumulate, max_total_debouncing_time, max_total_debounces_amount, skip_expr, cache_ttl, cache_ignore_s3_path, delete_after_secs, flow_env, priority, early_return, chat_input_enabled. For example, `chat_input_enabled: true` marks a flow as chat-style (flow-as-chat); keep it intact when restructuring such a flow.\n- `modules` contains normal sequential modules. Use top-level `preprocessor_module` and `failure_module` for special modules; do not put `preprocessor` or `failure` in `modules`.\n- Every module needs a stable unique `id` and a useful `summary` when the schema supports it.\n- Prefer path/script/flow modules when composing existing workspace logic. Use rawscript modules only when new inline code is needed.\n- When writing rawscript module code, call `get_instructions` with `subject: \"script\"` and the rawscript language first.\n\n## Groups and notes\n\n- Add `groups` to any non-trivial flow without being asked; \"Organizing Flows: Groups and Notes\" in the reference below gives the fields and the rules the editor enforces. Pass `null` to `write_flow` to clear them, and the same for `notes`.\n- With `patch_flow_json`, edit `groups` and `notes` the same way as any other field — they appear as top-level keys in the compact flow value.\n\n## Compact view: how rawscript bodies surface in tool I/O\n\n- `read_workspace_item` and `patch_flow_json` operate on a **compact view** of the flow: every rawscript module's `value.content` is replaced with the placeholder `\"inline_script.<moduleId>\"` so inline script bodies don't bloat tool I/O. Schema, groups, notes, preprocessor_module and failure_module are all shown in this view.\n- Inline rawscript content is **not** part of the JSON `patch_flow_json` sees. Edits to inline bodies happen via dedicated tools:\n  - `read_flow_module_code(path, module_id)` — returns the raw inline script content for one module.\n  - `set_flow_module_code(path, module_id, code)` — overwrites that module's inline script content; saves to the draft.\n- Use `patch_flow_json` for *structural* edits: module ids, paths, input_transforms, branch arrangement, summaries, preprocessor/failure swaps, schema/groups/notes. Use `set_flow_module_code` for changes inside a specific rawscript body.\n- When **adding a new rawscript module** with `patch_flow_json`, set its `content` to the placeholder `\"inline_script.<moduleId>\"` (matching the compact view) — never real code — then **immediately fill the body** with `set_flow_module_code(path, module_id, code)`. The module is saved with an empty body until you do; the tool result lists the module ids still awaiting code. A placeholder that names anything other than the module's own id or an existing rawscript module is rejected.\n- `write_flow` is for full overwrites / create-from-scratch. Its `modules`, `preprocessor_module`, and `failure_module` arguments are **non-compact** flow modules: inline each rawscript body directly in `content` by default. But if a body is long or quote/backslash-heavy enough that escaping it into the JSON string is error-prone — or if a `write_flow` call comes back with a JSON parse error — create that module with **empty content** (`\"content\": \"\"`) and fill it with `set_flow_module_code(path, module_id, code)`, whose `code` is a plain argument with no nested escaping. `write_flow` reports which modules still have empty bodies so you know what to fill.\n  - When overwriting an **existing** flow, set `\"content\": \"inline_script.<moduleId>\"` on any rawscript module whose code you are not changing — the placeholder resolves to that module's current body, so you never re-send (or re-read) unchanged code. Placeholders that match no existing rawscript module and are not the module's own id are rejected.\n\n\n# Windmill flow authoring reference\n\n# Windmill Flow Building Guide\n\n## OpenFlow Schema\n\nThe OpenFlow schema (openflow.openapi.yaml) is the source of truth for flow structure. Refer to OPENFLOW_SCHEMA for the complete type definitions.\n\n## Reserved Module IDs\n\n- `failure` - Reserved for failure handler module\n- `preprocessor` - Reserved for preprocessor module\n- `Input` - Reserved for flow input reference\n\n## Hard Structural Rules\n\nThese are strict Windmill schema rules. Follow them exactly.\n\n- `value.modules` is only for normal sequential steps\n- `value.preprocessor_module` and `value.failure_module` are special top-level fields inside `value`, not entries in `value.modules`\n- If a flow needs a preprocessor, create `value.preprocessor_module` with `id: preprocessor`\n- If a flow needs a failure handler, create `value.failure_module` with `id: failure`\n- Do NOT create regular modules inside `value.modules` named `preprocessor` or `failure`\n- `preprocessor_module` and `failure_module` only support `script` or `rawscript`\n- `preprocessor_module` runs before normal modules and cannot reference `results.*`\n- `failure_module` can use the `error` object with `error.message`, `error.step_id`, `error.name`, and `error.stack`\n- A flow whose `failure_module` runs still ends as failed, unless the handler returns an object with `recover: true`: the flow then ends as a success. This also holds when the failing step is inside a loop, a branch or a subflow. `recover` never changes which steps run; whether a loop or branch carries on past a failure is still decided by its own settings (`skip_failures`, `continue_on_error`)\n\nCorrect shape:\n\n```yaml\nvalue:\n  preprocessor_module:\n    id: preprocessor\n    value:\n      type: rawscript\n      ...\n  failure_module:\n    id: failure\n    value:\n      type: rawscript\n      ...\n  modules:\n    - id: process_event\n      value:\n        type: rawscript\n        ...\n```\n\nIncorrect shape:\n\n```yaml\nvalue:\n  modules:\n    - id: preprocessor\n      ...\n    - id: process_event\n      ...\n    - id: failure\n      ...\n```\n\n## Module ID Rules\n\n- Must be unique across the entire flow\n- Use underscores, not spaces (e.g., `fetch_data` not `fetch data`)\n- Use descriptive names that reflect the step's purpose\n\n## AI Agent Modules\n\nAn `aiagent` module runs an LLM that can call tools. Each entry of `value.tools` is a module-shaped\nobject with an extra `value.tool_type`: `flowmodule` for a script/flow tool, `mcp` for an MCP server\ntool, `websearch` for web search.\n\n```json\n{\n  \"id\": \"support_agent\",\n  \"summary\": \"AI agent for customer support\",\n  \"value\": {\n    \"type\": \"aiagent\",\n    \"input_transforms\": {\n      \"provider\": {\n        \"type\": \"static\",\n        \"value\": { \"kind\": \"openai\", \"resource\": \"$res:f/ai_providers/openai\", \"model\": \"gpt-4o\" }\n      },\n      \"output_type\": { \"type\": \"static\", \"value\": \"text\" },\n      \"user_message\": { \"type\": \"javascript\", \"expr\": \"flow_input.query\" },\n      \"system_prompt\": { \"type\": \"static\", \"value\": \"You are a helpful assistant.\" }\n    },\n    \"tools\": [\n      {\n        \"id\": \"search_docs\",\n        \"summary\": \"search_documentation\",\n        \"description\": \"Search the product documentation. Use it whenever the user asks how a feature works.\",\n        \"value\": {\n          \"tool_type\": \"flowmodule\",\n          \"type\": \"rawscript\",\n          \"language\": \"bun\",\n          \"content\": \"export async function main(query: string) { return ['doc1', 'doc2']; }\",\n          \"input_transforms\": { \"query\": { \"type\": \"static\", \"value\": \"\" } }\n        }\n      }\n    ]\n  }\n}\n```\n\n- `provider` is an object, not a bare resource string: `{ \"kind\": <provider kind>,\n  \"resource\": \"$res:<path>\", \"model\": <model id> }`. Required unless the module links to a saved\n  agent through `value.agent`. Static is right for a flow run from a form; a chat flow wires its\n  fields to flow inputs instead — see below\n\n### Chat-Mode Flows\n\nA flow with `value.chat_input_enabled: true` is run from a chat instead of a form: the composer\nsends one message per turn and renders the conversation. It needs a required `user_message` string\ninput, read by the agent. Any other flow input the composer does not edit itself is asked for\nunder Configure inputs.\n\n**A static `provider` gives a chat that cannot change its model.** Feed it from flow inputs\ninstead, either way round: one input carrying the whole object (`\"expr\": \"flow_input.model_config\"`)\nmakes every field editable, or wire it field by field to fix some and expose others. A field the\nchat can write becomes a control in the composer — a provider picker, a model list, a thinking\ncontrol — and a field left static is fixed, with no control drawn for it. `kind` is the one\nexception: the composer writes it only together with `resource`, since a provider is picked as a\npair, so a `kind` input wired on its own stays askable under Configure inputs and nothing the run\nneeds becomes unreachable.\n\n```json\n{\n  \"id\": \"chat_agent\",\n  \"value\": {\n    \"type\": \"aiagent\",\n    \"input_transforms\": {\n      \"provider\": {\n        \"type\": \"javascript\",\n        \"expr\": \"({ kind: 'anthropic', resource: '$res:f/ai/claude', model: flow_input.model, reasoning_effort: flow_input.thinking })\"\n      },\n      \"user_message\": { \"type\": \"javascript\", \"expr\": \"flow_input.user_message\" },\n      \"user_attachments\": { \"type\": \"javascript\", \"expr\": \"flow_input.files\" },\n      \"memory\": { \"type\": \"static\", \"value\": { \"kind\": \"compaction\" } },\n      \"streaming\": { \"type\": \"static\", \"value\": true },\n      \"output_type\": { \"type\": \"static\", \"value\": \"text\" }\n    },\n    \"tools\": []\n  }\n}\n```\n\n- Wiring field by field means one object literal whose values are literals or bare `flow_input.x`\n  references. A spread, a call or a computed key leaves the composer unable to tell which input\n  feeds which field, so it offers no control at all — a bare `flow_input.x` for the whole object\n  is read instead as that one input carrying every field\n- A step linked to a saved agent (`agent` set) has no `provider` of its own. It exposes the same\n  two controls through its `model` and `reasoning_effort` inputs, which replace what the agent\n  saved: `\"model\": { \"type\": \"javascript\", \"expr\": \"flow_input.model\" }`. An empty model keeps the\n  saved one, an empty effort sends none, and the provider resource always stays the agent's\n- `memory` is what lets the agent see earlier turns; without it every message starts from nothing\n- `streaming` on makes the answer and its thinking appear token by token instead of all at once\n- `user_attachments` points at a flow input typed as an array of s3 objects\n  (`{ \"type\": \"array\", \"items\": { \"type\": \"object\", \"resourceType\": \"s3object\" } }`), so files\n  sent with a message reach the agent\n- Running one needs a `memory_id` **query parameter** — not a flow argument — naming the\n  conversation the turn belongs to: a fresh UUID starts one, reusing a UUID continues it. The chat\n  supplies it itself; a run driven any other way has to pass it or the server refuses the job\n\n### Tool Naming Rules\n\nThese rules cover `flowmodule` tools, the ones the agent calls by name. A `websearch` tool's\n`summary` is a plain label (`Web Search`), and an `mcp` tool exposes the MCP server's own tool\nnames, so neither is name-checked at all — leave those summaries as they are.\n\n- A flowmodule tool's `summary` is the **name the agent calls it by**, not a human label. Put the\n  human-readable explanation in `description`\n- `summary` must match `^[a-zA-Z0-9_]+$`: letters, numbers and underscores only. No spaces, dashes,\n  dots or accents — `search_documentation`, never `Search documentation`\n- Always set `summary`. It must be unique among that agent's tools, and must not be one of the\n  reserved ids (`do`, `bg`, `ctx`, `state`, `if`, `else`, `for`, `delete`, `while`, `new`, `in`,\n  `failure`, `preprocessor`, `as`, `Input`, `Result`, `Trigger`)\n- A tool name outside that character set fails any run that offers the tool to the agent, with `Invalid tool name`.\n  The flow write tools refuse such a name.\n- Tool `id` follows the same rules as any module ID — unique across the flow, underscores not spaces\n- `description` is optional free text telling the agent when and how to call the tool. Set it\n  whenever the name alone does not make that obvious; it overrides the description derived from the\n  underlying script\n\n## AI Decision Modules\n\nAn `aidecision` module asks a decision model (TypeSafe's Jev, Cloudflare's Clef or OpenAI's Decisions API) typed questions about a `state` and\nanswers each with calibrated probabilities instead of text. Prefer it over an `aiagent` when the step\nis a judgment (classify, route, score or a yes/no check) that needs no tools and no free text: it\nis faster, cheaper, and its answers have a fixed shape.\n\n```json\n{\n  \"id\": \"triage\",\n  \"summary\": \"Classify the ticket\",\n  \"value\": {\n    \"type\": \"aidecision\",\n    \"input_transforms\": {\n      \"provider\": {\n        \"type\": \"static\",\n        \"value\": { \"kind\": \"typesafe\", \"resource\": \"$res:f/ai/typesafe\", \"model\": \"jev-latest\" }\n      },\n      \"state\": { \"type\": \"javascript\", \"expr\": \"flow_input.message\" },\n      \"questions\": {\n        \"type\": \"static\",\n        \"value\": {\n          \"intent\": {\n            \"type\": \"choice\",\n            \"instructions\": \"What does the customer want?\",\n            \"criteria\": { \"refund\": \"Money back\", \"bug\": \"Something is broken\", \"other\": \"Anything else\" }\n          },\n          \"urgency\": {\n            \"type\": \"score\",\n            \"instructions\": \"How urgent is the ticket?\",\n            \"criteria\": [\"Can wait\", \"This week\", \"Today\", \"Right now\"]\n          },\n          \"angry\": { \"type\": \"noul\", \"instructions\": \"Is the customer angry?\" }\n        }\n      }\n    }\n  }\n}\n```\n\n- `provider.kind` is `typesafe` (`model` `jev-latest` unless a version is pinned), `cloudflare`\n  (`model` `clef`, or `clef-flash` for faster answers) or `openai` (`model` `gpt-6-luna`, never a\n  chat model); the resource is of that same type\n- `state` is the content to evaluate: usually a text, such as the message to classify. To combine\n  several values, pass an object with descriptive keys holding only what the questions need\n  (`({ message: flow_input.message, plan: results.get_account.plan })`); an array of strings also works\n- `questions` maps each question name to `{ type, instructions, criteria }`:\n  - `choice`: `criteria` maps each option to its description (up to 255 options). The answer has\n    `choice`, `probabilities` and `confidence`\n  - `score`: `criteria` is an ordered array of 2 to 10 level descriptions. The answer has `score`,\n    `legend`, `probabilities` and `confidence`\n  - `noul`: yes/no, `criteria` optionally `{ \"true\": ..., \"false\": ... }` descriptions. The answer has\n    `noul`, the probability of yes from 0 to 1\n- The result is `{ output, model, usage }` with `output` keyed by question name, so a later step\n  reads `results.triage.output.intent.choice`, `results.triage.output.urgency.score` or\n  `results.triage.output.angry.noul > 0.7`\n- On `openai` the same `questions` are translated for its Decisions API and the answers read the\n  same way (`choice`, `score`, `noul`), except that `probabilities` is a list of\n  `{ value, probability }` (plus `label` on a score) and a score has no `legend`. A question OpenAI\n  refuses to answer fails the step. A `state` that is\n  an array of user messages (`[{ role: \"user\", content: [{ type: \"input_text\", text }, { type: \"input_image\", image_url: \"data:image/png;base64,...\" }] }]`)\n  is sent as is, which is how an image is evaluated; any other object or array is sent as JSON text\n\n### Branching on the Answers\n\nTo route on the answers, put a `branchone` right after the decision, one branch per option, with\neach `expr` reading the decision by id:\n\n```json\n{\n  \"id\": \"route\",\n  \"value\": {\n    \"type\": \"branchone\",\n    \"branches\": [\n      { \"summary\": \"refund\", \"expr\": \"results.triage.output.intent.choice === 'refund'\", \"modules\": [...] },\n      { \"summary\": \"bug\", \"expr\": \"results.triage.output.intent.choice === 'bug'\", \"modules\": [...] }\n    ],\n    \"default\": [...]\n  }\n}\n```\n\nFor a yes/no question, one branch with `\"expr\": \"results.triage.output.angry.noul >= 0.5\"` (tune the\nthreshold as needed) and the `default` as the \"no\" path.\n\n### As an Agent Tool\n\nAn `aidecision` can be a `flowmodule` tool of an `aiagent` (`\"tool_type\": \"flowmodule\", \"type\":\n\"aidecision\"`): set `state` to `{ \"type\": \"ai\" }` so the calling model supplies it, and only `output`\ngoes back to the model. The Tool Naming Rules apply to its `summary`. Only a flow's own agent step\ncan call one, not an agent used as a tool.\n\n## Common Mistakes to Avoid\n\n- Missing `input_transforms` - Rawscript parameters won't receive values without them\n- Referencing future steps - `results.step_id` only works for steps that execute before the current one\n- Duplicate module IDs - Each module ID must be unique in the flow\n- AI agent flowmodule tool names with spaces - `summary` is the tool name and only accepts letters, numbers and underscores\n\n## Data Flow Between Steps\n\n- `flow_input.property` - Access flow input parameters\n- `results.step_id` - Access output from a previous step only when that step result is in scope\n- `results.step_id.property` - Access specific property from a previous step output only when that step result is in scope\n- `flow_input.iter.value` - Current iteration value inside a `forloopflow`; in a `whileloopflow` it is just the iteration index (a plain number, same as `flow_input.iter.index`)\n- `flow_input.iter.index` - Current loop index when inside a loop (`forloopflow` or `whileloopflow`)\n- After a suspend step (approval), the next step's javascript expressions also get:\n  - `resume` - the last resume payload (the resume form values); with `continue_on_disapprove_timeout`, a disapproval or timeout yields `{ error: { name: \"SuspendedDisapproved\" | \"SuspendedTimedOut\", message } }` instead\n  - `resumes` - every resume payload, in arrival order\n  - `approvers` - a plain array of strings, one per resume: for a resume through the `resume`/`cancel` URL from `getResumeUrls(approver)`, the `approver` passed there if any; otherwise (including the `approvalPage` link) the resuming user's username, else `\"anonymous\"`, e.g. `approvers[0]`. It is NOT the `{ resume_id, approver }` objects stored in the flow status, so `approvers[0].approver` is always undefined\n\n## Loop Structure Rules\n\n- A `forloopflow` runs its `modules` once per element of `iterator`, a javascript expression returning an array (e.g. `results.get_items`); `parallel: true` runs the iterations concurrently, and `skip_failures: true` carries on past a failed iteration\n- For `whileloopflow`, break the loop with a module-level `stop_after_if`: on the loop module itself, or on an inner step (required when that step carries state via its own `results` — see below)\n- `stop_after_if` is always a sibling of `id` and `value` on a flow module — never a direct key of the loop's `value` object\n- `stop_after_all_iters_if` is for checks after the whole loop finishes, not the normal per-iteration break condition\n- `stop_after_if` is evaluated after each iteration: on the loop module, `result` is that iteration's result (what its last step returned); on an inner step, it is that step's result\n- `flow_input.iter.value` in a `whileloopflow` is just the iteration index (same number as `flow_input.iter.index`) — it never carries state, so `flow_input.iter.value.<field>` is always undefined and a loop whose stop condition depends on it never terminates\n- To carry state across iterations, a step reads its own previous-iteration result via `results.<its_own_id>` with a first-iteration fallback (e.g. `results.b ?? flow_input.start`) — but then the loop's `stop_after_if` MUST sit on that inner step, not on the loop module: a body that is exactly one plain step with the stop condition on the loop module runs on a fast path where `results.<step_id>` is null on every iteration and the loop never terminates (bodies with 2+ steps, or whose single step has its own `stop_after_if`, retry or similar, resolve `results` across iterations regardless of stop placement)\n- For state that is just a counter, derive it from the index instead (e.g. `flow_input.iter.index + 1`) — that works in every configuration, including with `stop_after_if` on the loop module\n- If the user asks for a final scalar/object after a loop, add a normal step after the loop that extracts the final value from the loop result instead of returning the whole loop result array\n\nCorrect `whileloopflow` shape:\n\n```yaml\n- id: loop_until_done\n  stop_after_if:\n    expr: result.done === true\n    skip_if_stopped: false\n  value:\n    type: whileloopflow\n    skip_failures: false\n    modules:\n      - id: advance_state\n        value:\n          type: rawscript\n          input_transforms:\n            count:\n              type: javascript\n              expr: flow_input.iter.index + 1\n- id: return_final_state\n  value:\n    type: rawscript\n    input_transforms:\n      final_state:\n        type: javascript\n        expr: results.loop_until_done[results.loop_until_done.length - 1]\n```\n\nCorrect `whileloopflow` shape carrying state via `results` (stop condition on the inner step):\n\n```yaml\n- id: loop_until_done\n  value:\n    type: whileloopflow\n    skip_failures: false\n    modules:\n      - id: advance_state\n        stop_after_if:\n          expr: result.done === true\n          skip_if_stopped: false\n        value:\n          type: rawscript\n          input_transforms:\n            state:\n              type: javascript\n              expr: results.advance_state ?? flow_input.initial_state\n```\n\nIncorrect `whileloopflow` patterns:\n\n```yaml\n- id: loop_until_done\n  value:\n    type: whileloopflow\n    stop_after_if:\n      expr: result.done === true\n```\n\n```yaml\ninput_transforms:\n  state:\n    type: javascript\n    # iter.value is a number (the iteration index); there is no previous-iteration state\n    expr: flow_input.iter.value.count\n```\n\n```yaml\ninput_transforms:\n  final_state:\n    type: javascript\n    expr: results.loop_until_done\n```\n\n## Approval / Suspend Structure\n\nAn approval step is a normal **script** step (`type: rawscript` or `type: script`) that is turned into an approval by adding a module-level `suspend`. Its script calls `wmill.getResumeUrls(approver)` to generate the secret resume/cancel URLs and returns them so they can be sent to the approver(s) (Slack, email, etc.) or approved from the run page.\n\n- `suspend` belongs on the flow module object itself, as a sibling of `id` and `value`\n- Never put `suspend` inside `value`\n- Do NOT use `type: identity` for an approval step. An identity step suspends but never produces the resume URLs, so approvers have no link to act on — it is not a functional approval.\n\nCorrect shape:\n\n```yaml\n- id: request_approval\n  suspend:\n    required_events: 1\n    resume_form:\n      schema:\n        type: object\n        properties:\n          comment:\n            type: string\n        required: [comment]\n  value:\n    type: rawscript\n    language: bun\n    input_transforms:\n      approver:\n        type: static\n        value: ''\n    content: |\n      import * as wmill from \"windmill-client\"\n\n      export async function main(approver?: string) {\n        const urls = await wmill.getResumeUrls(approver)\n        // send urls.resume / urls.cancel to the approver(s), e.g. via Slack or email\n        return urls\n      }\n```\n\nIncorrect shape (suspend misplaced inside `value`):\n\n```yaml\n- id: request_approval\n  value:\n    type: rawscript\n    suspend:\n      required_events: 1\n```\n\nIncorrect shape (identity has no resume URLs — not a real approval):\n\n```yaml\n- id: request_approval\n  suspend:\n    required_events: 1\n  value:\n    type: identity\n```\n\n## Branch Result Scope Rules\n\n- A `branchone` runs the first of its `branches` whose `expr` is true, in order, and its `default` modules when none is; a `branchall` runs every branch (concurrently with `parallel: true`)\n- Inside a branch, you may reference earlier outer steps and earlier steps in the same branch\n- Outside a `branchone`, do NOT reference ids of steps that only exist inside its branches or default branch. Use `results.<branchone_module_id>` instead\n- Outside a `branchall`, do NOT reference ids of steps inside its branches. Use `results.<branchall_module_id>` instead\n- If downstream steps need a stable shape after a branch, make each branch return the same fields\n- When needed, add a normalization step immediately after the branch and consume `results.<branch_module_id>` there\n\nCorrect after `branchone`:\n\n```yaml\n- id: route_order\n  value:\n    type: branchone\n    ...\n- id: send_confirmation\n  value:\n    input_transforms:\n      routed:\n        type: javascript\n        expr: results.route_order\n```\n\nIncorrect after `branchone`:\n\n```yaml\nexpr: results.create_shipment\nexpr: results.create_backorder\n```\n\nCorrect after `branchall`:\n\n```yaml\n- id: enrich_parallel\n  value:\n    type: branchall\n    parallel: true\n    ...\n- id: combine_data\n  value:\n    input_transforms:\n      enrichments:\n        type: javascript\n        expr: results.enrich_parallel\n```\n\n## Input Transforms\n\nEvery rawscript module needs `input_transforms` to map function parameters to values:\n\nStatic transform (fixed value):\n{\"param_name\": {\"type\": \"static\", \"value\": \"fixed_string\"}}\n\nJavaScript transform (dynamic expression):\n{\"param_name\": {\"type\": \"javascript\", \"expr\": \"results.previous_step.data\"}}\n\n## Resource References\n\n- For flow inputs: Use type `\"object\"` with format `\"resource-{type}\"` (e.g., `\"resource-postgresql\"`)\n- For step inputs: Use static value `\"$res:path/to/resource\"`\n\n## Reusing Existing Scripts and Flows\n\nUnless the user asked for new code, look for a workspace script or flow that already does a step's job before writing it, and reuse it by path instead of copying its logic into a rawscript:\n\n- a workspace script: `type: script` with `path` (e.g. `f/folder/send_email`)\n- a workspace flow, run as a subflow: `type: flow` with `path`\n- a Hub script: `type: script` with a `hub/<version>/<app>/<name>` path\n\nThe step's `input_transforms` must cover the reused item's inputs, so read its input schema first.\n\n## Additional Prompt for AI\n\nA flow's input schema may carry a top-level `prompt_for_ai` string, as a script's can: its author's instructions to an AI choosing the inputs. Follow it when you pick arguments to run that flow, and keep it when you rewrite the schema.\n\n## Organizing Flows: Groups and Notes\n\nGroups and notes shape how a flow reads in the editor; neither changes what it does.\n\n**Segment every non-trivial flow into groups without waiting to be asked.** Whenever a flow has more than a couple of steps, or consecutive steps form a stage (\"fetch\", \"transform\", \"notify\"), put them in a group, and aim for every meaningful step to belong to one. Use notes sparingly, for flow-wide information that belongs to no span of steps: the flow's purpose, key assumptions, warnings, TODOs. One note is usually enough; never label a run of steps with a note, which is what a group is for.\n\n`value.groups` lists the groups, each spanning the steps from `start_id` to `end_id`:\n\n- `start_id`, `end_id` (required): ids of the group's first and last step; the same id for both makes a one-step group\n- `summary`: the group's title\n- `note`: markdown shown under the title\n- `color`: one of `yellow`, `blue`, `green`, `purple`, `pink`, `orange`, `red`, `cyan`, `lime`, `gray`, never a hex code or CSS color; leave it out and the editor picks one\n- `autocollapse`: `true` shows the group collapsed by default\n\nThe editor refuses to draw a flow whose groups break any of these rules:\n\n- `start_id` and `end_id` are steps of the same list: both top-level, or both in the same loop body or branch. A group can hold a loop or branch step whole, but cannot start outside one and end inside it\n- `start_id` does not come after `end_id` in that list\n- groups nest (one entirely inside another) but never partly overlap, and no two groups share both `start_id` and `end_id`\n- groups hold ordinary steps only: never `preprocessor`, `failure`, `Input`, `Result`, `Trigger`, or an AI agent's tools\n\n`value.notes` lists sticky notes, each with a unique `id`, markdown `text`, a `color` from the same list, and `type: free`. The `group` note type is deprecated; use `value.groups` instead.\nLeave a note's `position` and `size` out and they are filled in for you.\n\n## Final Structural Self-Check\n\nBefore finalizing a flow, verify:\n\n- any preprocessor is in `value.preprocessor_module`\n- any failure handler is in `value.failure_module`\n- any approval step has module-level `suspend`\n- no downstream step references inner branch step ids from outside the branch\n- every AI agent flowmodule tool has a unique `summary` made only of letters, numbers and underscores\n- every group starts and ends on steps of the same list, start before end, nesting without partial overlap\n\n## S3 Object Operations\n\nWindmill provides built-in support for S3-compatible storage operations.\n\nTo accept an S3 object as flow input:\n\n```json\n{\n  \"type\": \"object\",\n  \"properties\": {\n    \"file\": {\n      \"type\": \"object\",\n      \"format\": \"resource-s3_object\",\n      \"description\": \"File to process\"\n    }\n  }\n}\n```\n\n## Using Resources in Flows\n\nOn Windmill, credentials and configuration are stored in resources. Resource types define the format of the resource.\n\n### As Flow Input\n\nIn the flow schema, set the property type to `\"object\"` with format `\"resource-{type}\"`:\n\n```json\n{\n  \"type\": \"object\",\n  \"properties\": {\n    \"database\": {\n      \"type\": \"object\",\n      \"format\": \"resource-postgresql\",\n      \"description\": \"Database connection\"\n    }\n  }\n}\n```\n\n### When No Resource Type Fits\n\nA connection still comes in as one resource input, not as separate string inputs (a URL, a token) or a variable read inside a step: type the input `resource-c_<name>` with a custom resource type holding the service's connection fields, its secret in a variable the resource references. The `c_` prefix of custom types keeps one apart from a hub type synced later under the same name.\n\n- If the instance is reported as not having synced the Windmill Hub's resource types, mention that a superadmin can sync them, since the hub has types for most services.\n- Otherwise, or when the user prefers it to syncing, propose the resource type and ask before creating it.\n- A plain variable is fine when the user asks for it.\n\n### As Step Input (Static Reference)\n\nReference a specific resource using `$res:` prefix:\n\n```json\n{\n  \"database\": {\n    \"type\": \"static\",\n    \"value\": \"$res:f/folder/my_database\"\n  }\n}\n```\n\n\n## OpenFlow Schema\n\n{\"OpenFlow\":{\"type\":\"object\",\"description\":\"Top-level flow definition containing metadata, configuration, and the flow structure\",\"properties\":{\"summary\":{\"type\":\"string\",\"description\":\"Short description of what this flow does\"},\"description\":{\"type\":\"string\",\"description\":\"Detailed documentation for this flow\"},\"value\":{\"$ref\":\"#/components/schemas/FlowValue\"},\"schema\":{\"type\":\"object\",\"description\":\"JSON Schema for flow inputs. Use this to define input parameters, their types, defaults, and validation. For resource inputs, set type to 'object' and format to 'resource-<type>' (e.g., 'resource-stripe')\"},\"on_behalf_of_email\":{\"type\":\"string\",\"description\":\"Address of the account the flow runs on behalf of. Derived from on_behalf_of on read; accepted on write, where it is resolved to the account it names.\"},\"on_behalf_of\":{\"type\":\"string\",\"description\":\"The flow runs with the permissions of this identity: u/{username}, g/{group}, or a bare email when the username is itself email-shaped. The only stored half of the identity; on_behalf_of_email is derived from it. Omit it when writing and it is resolved from that address instead.\"}},\"required\":[\"summary\",\"value\"]},\"FlowValue\":{\"type\":\"object\",\"description\":\"The flow structure containing modules and optional preprocessor/failure handlers\",\"properties\":{\"modules\":{\"type\":\"array\",\"description\":\"Array of steps that execute in sequence. Each step can be a script, subflow, loop, or branch\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}},\"failure_module\":{\"description\":\"Special module that executes when the flow fails. Receives error object with message, name, stack, and step_id. Must have id 'failure'. Only supports script/rawscript types\",\"$ref\":\"#/components/schemas/FlowModule\"},\"preprocessor_module\":{\"description\":\"Special module that runs before the first step on external triggers. Must have id 'preprocessor'. Only supports script/rawscript types. Cannot reference other step results\",\"$ref\":\"#/components/schemas/FlowModule\"},\"same_worker\":{\"type\":\"boolean\",\"description\":\"If true, all steps run on the same worker for better performance\"},\"preserve_step_tags\":{\"type\":\"boolean\",\"description\":\"If true and the flow runs on a custom worker tag, steps that declare their own non-empty tag run on it instead of inheriting the flow tag. Steps without their own tag still inherit the flow tag.\"},\"concurrent_limit\":{\"type\":\"number\",\"description\":\"Maximum number of concurrent executions of this flow\"},\"concurrency_key\":{\"type\":\"string\",\"description\":\"Expression to group concurrent executions (e.g., by user ID)\"},\"concurrency_time_window_s\":{\"type\":\"number\",\"description\":\"Time window in seconds for concurrent_limit\"},\"debounce_delay_s\":{\"type\":\"integer\",\"description\":\"Delay in seconds to debounce flow executions\"},\"debounce_key\":{\"type\":\"string\",\"description\":\"Expression to group debounced executions\"},\"debounce_args_to_accumulate\":{\"type\":\"array\",\"description\":\"Arguments to accumulate across debounced executions\",\"items\":{\"type\":\"string\"}},\"max_total_debouncing_time\":{\"type\":\"integer\",\"description\":\"Maximum total time in seconds that a job can be debounced\"},\"max_total_debounces_amount\":{\"type\":\"integer\",\"description\":\"Maximum number of times a job can be debounced\"},\"skip_expr\":{\"type\":\"string\",\"description\":\"JavaScript expression to conditionally skip the entire flow\"},\"cache_ttl\":{\"type\":\"number\",\"description\":\"Cache duration in seconds for flow results\"},\"cache_ignore_s3_path\":{\"type\":\"boolean\"},\"delete_after_secs\":{\"type\":\"integer\",\"description\":\"If set, delete the flow job's args, result and logs after this many seconds following job completion\"},\"flow_env\":{\"type\":\"object\",\"description\":\"Environment variables available to all steps. Values can be strings, JSON values, or special references: '$var:path' (workspace variable) or '$res:path' (resource).\",\"additionalProperties\":{}},\"priority\":{\"type\":\"number\",\"description\":\"Execution priority (higher numbers run first)\"},\"early_return\":{\"type\":\"string\",\"description\":\"Id of the step whose result a sync webhook returns as soon as that step completes, while the rest of the flow keeps running. When the step does not run, or fails while the flow has a failure module, the flow's final result is returned instead.\"},\"chat_input_enabled\":{\"type\":\"boolean\",\"description\":\"Whether this flow accepts chat-style input\"},\"notes\":{\"type\":\"array\",\"description\":\"Sticky notes attached to the flow\",\"items\":{\"$ref\":\"#/components/schemas/FlowNote\"}},\"groups\":{\"type\":\"array\",\"description\":\"Semantic groups of modules for organizational purposes\",\"items\":{\"$ref\":\"#/components/schemas/FlowGroup\"}}},\"required\":[\"modules\"]},\"Retry\":{\"type\":\"object\",\"description\":\"Retry configuration for failed module executions\",\"properties\":{\"constant\":{\"type\":\"object\",\"description\":\"Retry with constant delay between attempts\",\"properties\":{\"attempts\":{\"type\":\"integer\",\"description\":\"Number of retry attempts\"},\"seconds\":{\"type\":\"integer\",\"description\":\"Seconds to wait between retries\"}}},\"exponential\":{\"type\":\"object\",\"description\":\"Retry with exponential backoff: the n-th retry waits multiplier \\u00d7 seconds^n\",\"properties\":{\"attempts\":{\"type\":\"integer\",\"description\":\"Number of retry attempts\"},\"multiplier\":{\"type\":\"integer\",\"description\":\"Multiplier for exponential backoff\"},\"seconds\":{\"type\":\"integer\",\"minimum\":1,\"description\":\"Base of the exponential (seconds); the n-th retry waits multiplier \\u00d7 seconds^n, where n counts constant retries too\"},\"random_factor\":{\"type\":\"integer\",\"minimum\":0,\"maximum\":100,\"description\":\"Random jitter percentage (0-100) to avoid thundering herd\"}}},\"retry_if\":{\"$ref\":\"#/components/schemas/RetryIf\"}}},\"FlowNote\":{\"type\":\"object\",\"description\":\"A sticky note attached to a flow for documentation and annotation\",\"properties\":{\"id\":{\"type\":\"string\",\"description\":\"Unique identifier for the note\"},\"text\":{\"type\":\"string\",\"description\":\"Content of the note\"},\"position\":{\"type\":\"object\",\"description\":\"Position of the note in the flow editor\",\"properties\":{\"x\":{\"type\":\"number\",\"description\":\"X coordinate\"},\"y\":{\"type\":\"number\",\"description\":\"Y coordinate\"}},\"required\":[\"x\",\"y\"]},\"size\":{\"type\":\"object\",\"description\":\"Size of the note in the flow editor\",\"properties\":{\"width\":{\"type\":\"number\",\"description\":\"Width in pixels\"},\"height\":{\"type\":\"number\",\"description\":\"Height in pixels\"}},\"required\":[\"width\",\"height\"]},\"color\":{\"type\":\"string\",\"description\":\"Color of the note, one of: yellow, blue, green, purple, pink, orange, red, cyan, lime, gray. Any other value renders unstyled.\"},\"type\":{\"type\":\"string\",\"enum\":[\"free\",\"group\"],\"description\":\"Type of note - 'free' for standalone notes. 'group' notes are deprecated; segment a flow with FlowValue.groups instead.\"},\"locked\":{\"type\":\"boolean\",\"default\":false,\"description\":\"Whether the note is locked and cannot be edited or moved\"},\"contained_node_ids\":{\"type\":\"array\",\"items\":{\"type\":\"string\"},\"description\":\"For group notes, the IDs of nodes contained within this group\"}},\"required\":[\"id\",\"text\",\"color\",\"type\"]},\"FlowGroup\":{\"type\":\"object\",\"description\":\"A semantic group of flow modules for organizational purposes. Does not affect execution \\u2014 modules remain in their original position in the flow. Groups provide naming and collapsibility in the editor. Members are computed dynamically from all nodes on paths between start_id and end_id.\",\"properties\":{\"summary\":{\"type\":\"string\",\"description\":\"Display name for this group\"},\"note\":{\"type\":\"string\",\"description\":\"Markdown note shown below the group header\"},\"autocollapse\":{\"type\":\"boolean\",\"default\":false,\"description\":\"If true, this group is collapsed by default in the flow editor. UI hint only.\"},\"start_id\":{\"type\":\"string\",\"description\":\"ID of the first flow module in this group (topological entry point)\"},\"end_id\":{\"type\":\"string\",\"description\":\"ID of the last flow module in this group (topological exit point)\"},\"color\":{\"type\":\"string\",\"description\":\"Color for the group in the flow editor, one of: yellow, blue, green, purple, pink, orange, red, cyan, lime, gray. Omit it to let the editor pick one.\"}},\"required\":[\"start_id\",\"end_id\"]},\"RetryIf\":{\"type\":\"object\",\"description\":\"Conditional retry based on error or result\",\"properties\":{\"expr\":{\"type\":\"string\",\"description\":\"JavaScript expression that returns true to retry. Has access to 'result' and 'error' variables\"}},\"required\":[\"expr\"]},\"StopAfterIf\":{\"type\":\"object\",\"description\":\"Early termination condition for a module\",\"properties\":{\"skip_if_stopped\":{\"type\":\"boolean\",\"description\":\"If true, following steps are skipped when this condition triggers\"},\"expr\":{\"type\":\"string\",\"description\":\"JavaScript expression evaluated after the module runs. Can use 'result' (step's result) or 'flow_input'. Return true to stop\"},\"error_message\":{\"type\":\"string\",\"nullable\":true,\"description\":\"Custom error message when stopping with an error. Mutually exclusive with skip_if_stopped. If set to a non-empty string, the flow stops with this error. If empty string, a default error message is used. If null or omitted, no error is raised.\"},\"error_include_result\":{\"type\":\"boolean\",\"description\":\"When stopping with an error (error_message set), embed the stopping step's own result inside the raised error object (as error.result) instead of discarding it. The top-level result stays { error }. Defaults to false.\"}},\"required\":[\"expr\"]},\"FlowModule\":{\"type\":\"object\",\"description\":\"A single step in a flow. Can be a script, subflow, loop, or branch\",\"properties\":{\"id\":{\"type\":\"string\",\"description\":\"Unique identifier for this step. Used to reference results via 'results.step_id'. Must be a valid identifier (alphanumeric, underscore, hyphen)\"},\"value\":{\"$ref\":\"#/components/schemas/FlowModuleValue\"},\"stop_after_if\":{\"description\":\"Early termination condition evaluated after this step completes\",\"$ref\":\"#/components/schemas/StopAfterIf\"},\"stop_after_all_iters_if\":{\"description\":\"For loops only - early termination condition evaluated after all iterations complete\",\"$ref\":\"#/components/schemas/StopAfterIf\"},\"skip_if\":{\"type\":\"object\",\"description\":\"Conditionally skip this step based on previous results or flow inputs\",\"properties\":{\"expr\":{\"type\":\"string\",\"description\":\"JavaScript expression that returns true to skip. Can use 'flow_input' or 'results.<step_id>'\"}},\"required\":[\"expr\"]},\"sleep\":{\"description\":\"Delay before executing this step (in seconds or as expression)\",\"$ref\":\"#/components/schemas/InputTransform\"},\"cache_ttl\":{\"type\":\"number\",\"description\":\"Cache duration in seconds for this step's results\"},\"cache_ignore_s3_path\":{\"type\":\"boolean\"},\"timeout\":{\"description\":\"Maximum execution time in seconds (static value or expression)\",\"$ref\":\"#/components/schemas/InputTransform\"},\"delete_after_secs\":{\"type\":\"integer\",\"description\":\"If set, delete the step's args, result and logs after this many seconds following job completion\"},\"summary\":{\"type\":\"string\",\"description\":\"Short description of what this step does\"},\"mock\":{\"type\":\"object\",\"description\":\"Mock configuration for testing without executing the actual step\",\"properties\":{\"enabled\":{\"type\":\"boolean\",\"description\":\"If true, return mock value instead of executing\"},\"return_value\":{\"description\":\"Value to return when mocked\"}}},\"suspend\":{\"type\":\"object\",\"description\":\"Configuration for approval/resume steps that wait for user input\",\"properties\":{\"required_events\":{\"type\":\"integer\",\"description\":\"Number of approvals required before continuing\"},\"timeout\":{\"type\":\"integer\",\"description\":\"Timeout in seconds before auto-continuing or canceling\"},\"resume_form\":{\"type\":\"object\",\"description\":\"Form schema for collecting input when resuming\",\"properties\":{\"schema\":{\"type\":\"object\",\"description\":\"JSON Schema for the resume form\"}}},\"user_auth_required\":{\"type\":\"boolean\",\"description\":\"If true, only authenticated users can approve\"},\"user_groups_required\":{\"description\":\"Expression or list of groups that can approve\",\"$ref\":\"#/components/schemas/InputTransform\"},\"self_approval_disabled\":{\"type\":\"boolean\",\"description\":\"If true, the user who started the flow cannot approve\"},\"hide_cancel\":{\"type\":\"boolean\",\"description\":\"If true, hide the cancel button on the approval form\"},\"continue_on_disapprove_timeout\":{\"type\":\"boolean\",\"description\":\"If true, continue flow on timeout instead of canceling\"},\"skin\":{\"type\":\"string\",\"enum\":[\"detailed\",\"minimal\"],\"description\":\"How the approval request is presented, on the approval page and in Slack/Teams approval messages. 'detailed' (used when unset) shows the flow details (arguments, graph, approvers); 'minimal' shows only the request: the step description, form and approve/reject actions\"}}},\"priority\":{\"type\":\"number\",\"description\":\"Execution priority for this step (higher numbers run first)\"},\"continue_on_error\":{\"type\":\"boolean\",\"description\":\"If true, flow continues even if this step fails\"},\"job_token_scopes\":{\"type\":\"array\",\"nullable\":true,\"items\":{\"type\":\"string\"},\"description\":\"Scopes the token of this step's jobs is restricted to (domain:action[:resource], e.g. oidc:write), on top of the flow's own restriction: a step can only narrow it. Applies to everything the step runs (loop and branch bodies, AI agent tools).\"},\"retry\":{\"description\":\"Retry configuration if this step fails\",\"$ref\":\"#/components/schemas/Retry\"},\"debouncing\":{\"description\":\"Debounce configuration for this step (EE only)\",\"type\":\"object\",\"properties\":{\"debounce_delay_s\":{\"type\":\"integer\",\"description\":\"Delay in seconds to debounce this step's executions across flow runs\"},\"debounce_key\":{\"type\":\"string\",\"description\":\"Expression to group debounced executions. Supports $workspace and $args[name]. Default: $workspace/flow/<flow_path>-<step_id>\"},\"debounce_args_to_accumulate\":{\"type\":\"array\",\"description\":\"Array-type arguments to accumulate across debounced executions\",\"items\":{\"type\":\"string\"}},\"max_total_debouncing_time\":{\"type\":\"integer\",\"description\":\"Maximum total time in seconds before forced execution\"},\"max_total_debounces_amount\":{\"type\":\"integer\",\"description\":\"Maximum number of debounces before forced execution\"}}}},\"required\":[\"value\",\"id\"]},\"InputTransform\":{\"description\":\"Maps input parameters for a step. Can be a static value or a JavaScript expression that references previous results or flow inputs\",\"oneOf\":[{\"$ref\":\"#/components/schemas/StaticTransform\"},{\"$ref\":\"#/components/schemas/JavascriptTransform\"},{\"$ref\":\"#/components/schemas/AiTransform\"}],\"discriminator\":{\"propertyName\":\"type\",\"mapping\":{\"static\":\"#/components/schemas/StaticTransform\",\"javascript\":\"#/components/schemas/JavascriptTransform\",\"ai\":\"#/components/schemas/AiTransform\"}}},\"StaticTransform\":{\"type\":\"object\",\"description\":\"Static value passed directly to the step. Use for hardcoded values or resource references like '$res:path/to/resource'\",\"properties\":{\"value\":{\"description\":\"The static value. For resources, use format '$res:path/to/resource'\"},\"type\":{\"type\":\"string\",\"enum\":[\"static\"]}},\"required\":[\"type\"]},\"JavascriptTransform\":{\"type\":\"object\",\"description\":\"JavaScript expression evaluated at runtime. Can reference previous step results via 'results.step_id' or flow inputs via 'flow_input.property'. Inside for loops, use 'flow_input.iter.value' for the current iteration value (in while loops it equals 'flow_input.iter.index')\",\"properties\":{\"expr\":{\"type\":\"string\",\"description\":\"JavaScript expression returning the value. Available variables - results (object with all previous step results), flow_input (flow inputs), flow_input.iter (in loops)\"},\"type\":{\"type\":\"string\",\"enum\":[\"javascript\"]}},\"required\":[\"expr\",\"type\"]},\"AiTransform\":{\"type\":\"object\",\"description\":\"Value resolved by the AI runtime for this input. The AI engine decides how to satisfy the parameter.\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"ai\"]}},\"required\":[\"type\"]},\"AIProviderKind\":{\"type\":\"string\",\"description\":\"Supported AI provider types\",\"enum\":[\"openai\",\"azure_openai\",\"azure_foundry\",\"anthropic\",\"mistral\",\"deepseek\",\"googleai\",\"groq\",\"openrouter\",\"togetherai\",\"customai\",\"aws_bedrock\",\"typesafe\",\"cloudflare\"]},\"ProviderConfig\":{\"type\":\"object\",\"description\":\"Complete AI provider configuration with resource reference and model selection\",\"properties\":{\"kind\":{\"$ref\":\"#/components/schemas/AIProviderKind\"},\"resource\":{\"type\":\"string\",\"description\":\"Resource reference in format '$res:{resource_path}' pointing to provider credentials\"},\"model\":{\"type\":\"string\",\"description\":\"Model identifier (e.g., 'gpt-4', 'claude-3-opus-20240229', 'gemini-pro')\"},\"reasoning_effort\":{\"type\":\"string\",\"description\":\"Provider-native reasoning effort token (e.g. 'low', 'high', 'none') for models that support extended thinking. Optional; unset leaves the provider default.\"}},\"required\":[\"kind\",\"resource\",\"model\"]},\"StaticProviderTransform\":{\"type\":\"object\",\"description\":\"Static provider configuration passed directly to the AI agent\",\"properties\":{\"value\":{\"$ref\":\"#/components/schemas/ProviderConfig\"},\"type\":{\"type\":\"string\",\"enum\":[\"static\"]}},\"required\":[\"type\",\"value\"]},\"ProviderTransform\":{\"description\":\"Provider configuration - can be static (ProviderConfig), JavaScript expression, or AI-determined\",\"oneOf\":[{\"$ref\":\"#/components/schemas/StaticProviderTransform\"},{\"$ref\":\"#/components/schemas/JavascriptTransform\"},{\"$ref\":\"#/components/schemas/AiTransform\"}],\"discriminator\":{\"propertyName\":\"type\",\"mapping\":{\"static\":\"#/components/schemas/StaticProviderTransform\",\"javascript\":\"#/components/schemas/JavascriptTransform\",\"ai\":\"#/components/schemas/AiTransform\"}}},\"MemoryOff\":{\"type\":\"object\",\"description\":\"No conversation memory/context\",\"properties\":{\"kind\":{\"type\":\"string\",\"enum\":[\"off\"]}},\"required\":[\"kind\"]},\"MemoryWindow\":{\"type\":\"object\",\"description\":\"Keeps the most recent messages of the memory named by the run's memory id (or the step's\\n`memory_id`). Without a memory id the agent runs without memory.\\n\",\"properties\":{\"kind\":{\"type\":\"string\",\"enum\":[\"window\"]},\"context_length\":{\"type\":\"integer\",\"description\":\"Number of most recent messages to load and store. 0 turns memory off.\"}},\"required\":[\"kind\",\"context_length\"]},\"MemoryAuto\":{\"type\":\"object\",\"deprecated\":true,\"description\":\"Deprecated, still read as it was written: the run's memory id, else the `memory_id` here.\\nThe step's own `memory_id` is not read while this kind is set; switch the kind to `window`\\nto use it. Without a `context_length`, or with 0, it is `off` and reads `previous_messages`.\\n\",\"properties\":{\"kind\":{\"type\":\"string\",\"enum\":[\"auto\"]},\"context_length\":{\"type\":\"integer\",\"description\":\"Maximum number of messages to retain in context\"},\"memory_id\":{\"type\":\"string\",\"description\":\"Identifier for persistent memory across agent invocations\"}},\"required\":[\"kind\"]},\"MemoryCompaction\":{\"type\":\"object\",\"description\":\"Keeps the whole memory named by the run's memory id (or the step's `memory_id`), replacing\\nits older part with a summary as the conversation approaches the model's context window.\\nWithout a memory id the agent runs without memory, and compaction bounds the run's own loop.\\n\",\"properties\":{\"kind\":{\"type\":\"string\",\"enum\":[\"compaction\"]},\"context_window\":{\"type\":\"integer\",\"description\":\"Overrides the context window looked up from the model, in tokens. Only a model\\nWindmill does not know needs one; those fall back to 128000.\\n\"}},\"required\":[\"kind\"]},\"MemoryMessage\":{\"type\":\"object\",\"description\":\"A single message in conversation history\",\"properties\":{\"role\":{\"type\":\"string\",\"enum\":[\"user\",\"assistant\",\"system\"]},\"content\":{\"type\":\"string\"}},\"required\":[\"role\",\"content\"]},\"MemoryManual\":{\"type\":\"object\",\"deprecated\":true,\"description\":\"Deprecated, still read as it was written. Move the step to `off` with `previous_messages` instead.\",\"properties\":{\"kind\":{\"type\":\"string\",\"enum\":[\"manual\"]},\"messages\":{\"type\":\"array\",\"items\":{\"$ref\":\"#/components/schemas/MemoryMessage\"}}},\"required\":[\"kind\",\"messages\"]},\"MemoryConfig\":{\"description\":\"Managed memory, stored by Windmill and replayed with each request. The memory is named by a memory id, see `memory_id`. While it is off, a step can supply its history in `previous_messages`.\",\"oneOf\":[{\"$ref\":\"#/components/schemas/MemoryOff\"},{\"$ref\":\"#/components/schemas/MemoryWindow\"},{\"$ref\":\"#/components/schemas/MemoryCompaction\"},{\"$ref\":\"#/components/schemas/MemoryAuto\"},{\"$ref\":\"#/components/schemas/MemoryManual\"}],\"discriminator\":{\"propertyName\":\"kind\",\"mapping\":{\"off\":\"#/components/schemas/MemoryOff\",\"window\":\"#/components/schemas/MemoryWindow\",\"compaction\":\"#/components/schemas/MemoryCompaction\",\"auto\":\"#/components/schemas/MemoryAuto\",\"manual\":\"#/components/schemas/MemoryManual\"}}},\"StaticMemoryTransform\":{\"type\":\"object\",\"description\":\"Static memory configuration passed directly to the AI agent\",\"properties\":{\"value\":{\"$ref\":\"#/components/schemas/MemoryConfig\"},\"type\":{\"type\":\"string\",\"enum\":[\"static\"]}},\"required\":[\"type\",\"value\"]},\"MemoryTransform\":{\"description\":\"Memory configuration - can be static (MemoryConfig), JavaScript expression, or AI-determined\",\"oneOf\":[{\"$ref\":\"#/components/schemas/StaticMemoryTransform\"},{\"$ref\":\"#/components/schemas/JavascriptTransform\"},{\"$ref\":\"#/components/schemas/AiTransform\"}],\"discriminator\":{\"propertyName\":\"type\",\"mapping\":{\"static\":\"#/components/schemas/StaticMemoryTransform\",\"javascript\":\"#/components/schemas/JavascriptTransform\",\"ai\":\"#/components/schemas/AiTransform\"}}},\"FlowModuleValue\":{\"description\":\"The actual implementation of a flow step. Can be a script (inline or referenced), subflow, loop, branch, or special module type\",\"oneOf\":[{\"$ref\":\"#/components/schemas/RawScript\"},{\"$ref\":\"#/components/schemas/PathScript\"},{\"$ref\":\"#/components/schemas/PathFlow\"},{\"$ref\":\"#/components/schemas/ForloopFlow\"},{\"$ref\":\"#/components/schemas/WhileloopFlow\"},{\"$ref\":\"#/components/schemas/BranchOne\"},{\"$ref\":\"#/components/schemas/BranchAll\"},{\"$ref\":\"#/components/schemas/Identity\"},{\"$ref\":\"#/components/schemas/AiAgent\"},{\"$ref\":\"#/components/schemas/AiDecision\"}],\"discriminator\":{\"propertyName\":\"type\",\"mapping\":{\"rawscript\":\"#/components/schemas/RawScript\",\"script\":\"#/components/schemas/PathScript\",\"flow\":\"#/components/schemas/PathFlow\",\"forloopflow\":\"#/components/schemas/ForloopFlow\",\"whileloopflow\":\"#/components/schemas/WhileloopFlow\",\"branchone\":\"#/components/schemas/BranchOne\",\"branchall\":\"#/components/schemas/BranchAll\",\"identity\":\"#/components/schemas/Identity\",\"aiagent\":\"#/components/schemas/AiAgent\",\"aidecision\":\"#/components/schemas/AiDecision\"}}},\"RawScript\":{\"type\":\"object\",\"description\":\"Inline script with code defined directly in the flow. Use 'bun' as default language if unspecified. The script receives arguments from input_transforms\",\"properties\":{\"input_transforms\":{\"type\":\"object\",\"description\":\"Map of parameter names to their values (static or JavaScript expressions). These become the script's input arguments\",\"additionalProperties\":{\"$ref\":\"#/components/schemas/InputTransform\"}},\"content\":{\"type\":\"string\",\"description\":\"The script source code. Should export a 'main' function\"},\"language\":{\"type\":\"string\",\"description\":\"Programming language for this script\",\"enum\":[\"deno\",\"bun\",\"bunnative\",\"python3\",\"go\",\"bash\",\"powershell\",\"postgresql\",\"mysql\",\"bigquery\",\"snowflake\",\"mssql\",\"oracledb\",\"graphql\",\"nativets\",\"php\",\"rust\",\"ansible\",\"csharp\",\"nu\",\"java\",\"ruby\",\"rlang\",\"duckdb\"]},\"path\":{\"type\":\"string\",\"description\":\"Optional path for saving this script\"},\"lock\":{\"type\":\"string\",\"description\":\"Lock file content for dependencies\"},\"type\":{\"type\":\"string\",\"enum\":[\"rawscript\"]},\"tag\":{\"type\":\"string\",\"description\":\"Worker group tag for execution routing\"},\"concurrent_limit\":{\"type\":\"number\",\"description\":\"Maximum concurrent executions of this script\"},\"concurrency_time_window_s\":{\"type\":\"number\",\"description\":\"Time window for concurrent_limit\"},\"custom_concurrency_key\":{\"type\":\"string\",\"description\":\"Custom key for grouping concurrent executions\"},\"is_trigger\":{\"type\":\"boolean\",\"description\":\"If true, this script is a trigger that can start the flow\"},\"assets\":{\"type\":\"array\",\"description\":\"External resources this script accesses (S3 objects, resources, etc.)\",\"items\":{\"type\":\"object\",\"required\":[\"path\",\"kind\"],\"properties\":{\"path\":{\"type\":\"string\",\"description\":\"Path to the asset\"},\"kind\":{\"type\":\"string\",\"description\":\"Type of asset\",\"enum\":[\"s3object\",\"resource\",\"ducklake\",\"datatable\",\"volume\",\"dbt\"]},\"access_type\":{\"type\":\"string\",\"nullable\":true,\"description\":\"Access level for this asset\",\"enum\":[\"r\",\"w\",\"rw\"]},\"alt_access_type\":{\"type\":\"string\",\"nullable\":true,\"description\":\"Alternative access level\",\"enum\":[\"r\",\"w\",\"rw\"]}}}}},\"required\":[\"type\",\"content\",\"language\",\"input_transforms\"]},\"PathScript\":{\"type\":\"object\",\"description\":\"Reference to an existing script by path. Use this when calling a previously saved script instead of writing inline code\",\"properties\":{\"input_transforms\":{\"type\":\"object\",\"description\":\"Map of parameter names to their values (static or JavaScript expressions). These become the script's input arguments\",\"additionalProperties\":{\"$ref\":\"#/components/schemas/InputTransform\"}},\"path\":{\"type\":\"string\",\"description\":\"Path to the script in the workspace (e.g., 'f/scripts/send_email')\"},\"hash\":{\"type\":\"string\",\"description\":\"Optional specific version hash of the script to use\"},\"type\":{\"type\":\"string\",\"enum\":[\"script\"]},\"tag_override\":{\"type\":\"string\",\"description\":\"Override the script's default worker group tag\"},\"is_trigger\":{\"type\":\"boolean\",\"description\":\"If true, this script is a trigger that can start the flow\"}},\"required\":[\"type\",\"path\",\"input_transforms\"]},\"PathFlow\":{\"type\":\"object\",\"description\":\"Reference to an existing flow by path. Use this to call another flow as a subflow\",\"properties\":{\"input_transforms\":{\"type\":\"object\",\"description\":\"Map of parameter names to their values (static or JavaScript expressions). These become the subflow's input arguments\",\"additionalProperties\":{\"$ref\":\"#/components/schemas/InputTransform\"}},\"path\":{\"type\":\"string\",\"description\":\"Path to the flow in the workspace (e.g., 'f/flows/process_user')\"},\"type\":{\"type\":\"string\",\"enum\":[\"flow\"]}},\"required\":[\"type\",\"path\",\"input_transforms\"]},\"ForloopFlow\":{\"type\":\"object\",\"description\":\"Executes nested modules in a loop over an iterator. Inside the loop, use 'flow_input.iter.value' to access the current iteration value, and 'flow_input.iter.index' for the index. Supports parallel execution for better performance on I/O-bound operations\",\"properties\":{\"modules\":{\"type\":\"array\",\"description\":\"Steps to execute for each iteration. These can reference the iteration value via 'flow_input.iter.value'\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}},\"iterator\":{\"description\":\"JavaScript expression that returns an array to iterate over. Can reference 'results.step_id' or 'flow_input'\",\"$ref\":\"#/components/schemas/InputTransform\"},\"skip_failures\":{\"type\":\"boolean\",\"description\":\"If true, iteration failures don't stop the loop. Failed iterations return null\"},\"type\":{\"type\":\"string\",\"enum\":[\"forloopflow\"]},\"parallel\":{\"type\":\"boolean\",\"description\":\"If true, iterations run concurrently (faster for I/O-bound operations). Use with parallelism to control concurrency\"},\"parallelism\":{\"description\":\"Maximum number of concurrent iterations when parallel=true. Limits resource usage. Can be static number or expression\",\"$ref\":\"#/components/schemas/InputTransform\"},\"squash\":{\"type\":\"boolean\"}},\"required\":[\"modules\",\"iterator\",\"skip_failures\",\"type\"]},\"WhileloopFlow\":{\"type\":\"object\",\"description\":\"Executes nested modules repeatedly until stopped. The implicit iterator is the iteration counter, so 'flow_input.iter.value' equals 'flow_input.iter.index' (0, 1, 2, ...) and never carries state. To carry state across iterations, a step reads its own previous-iteration result via 'results.<its_own_id>' with a first-iteration fallback - the loop's stop_after_if must then be on that inner step (a plain single-step body with stop_after_if on the loop module does not resolve 'results' across iterations and never terminates); plain counters can instead be derived from 'flow_input.iter.index', which works in every configuration. stop_after_if is evaluated after each iteration - on the loop module 'result' is the last iteration's result\",\"properties\":{\"modules\":{\"type\":\"array\",\"description\":\"Steps to execute in each iteration\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}},\"skip_failures\":{\"type\":\"boolean\",\"description\":\"If true, iteration failures don't stop the loop. Failed iterations return null\"},\"type\":{\"type\":\"string\",\"enum\":[\"whileloopflow\"]},\"parallel\":{\"type\":\"boolean\",\"description\":\"If true, iterations run concurrently (use with caution in while loops)\"},\"parallelism\":{\"description\":\"Maximum number of concurrent iterations when parallel=true\",\"$ref\":\"#/components/schemas/InputTransform\"},\"squash\":{\"type\":\"boolean\"}},\"required\":[\"modules\",\"skip_failures\",\"type\"]},\"BranchOne\":{\"type\":\"object\",\"description\":\"Conditional branching where only the first matching branch executes. Branches are evaluated in order, and the first one with a true expression runs. If no branches match, the default branch executes\",\"properties\":{\"branches\":{\"type\":\"array\",\"description\":\"Array of branches to evaluate in order. The first branch with expr evaluating to true executes\",\"items\":{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\",\"description\":\"Short description of this branch condition\"},\"expr\":{\"type\":\"string\",\"description\":\"JavaScript expression that returns boolean. Can use 'results.step_id' or 'flow_input'. First true expr wins\"},\"modules\":{\"type\":\"array\",\"description\":\"Steps to execute if this branch's expr is true\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}}},\"required\":[\"modules\",\"expr\"]}},\"default\":{\"type\":\"array\",\"description\":\"Steps to execute if no branch expressions match\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}},\"type\":{\"type\":\"string\",\"enum\":[\"branchone\"]}},\"required\":[\"branches\",\"default\",\"type\"]},\"BranchAll\":{\"type\":\"object\",\"description\":\"Parallel branching where all branches execute simultaneously. Unlike BranchOne, all branches run regardless of conditions. Useful for executing independent tasks concurrently\",\"properties\":{\"branches\":{\"type\":\"array\",\"description\":\"Array of branches that all execute (either in parallel or sequentially)\",\"items\":{\"type\":\"object\",\"properties\":{\"summary\":{\"type\":\"string\",\"description\":\"Short description of this branch's purpose\"},\"skip_failure\":{\"type\":\"boolean\",\"description\":\"If true, failure in this branch doesn't fail the entire flow\"},\"modules\":{\"type\":\"array\",\"description\":\"Steps to execute in this branch\",\"items\":{\"$ref\":\"#/components/schemas/FlowModule\"}}},\"required\":[\"modules\"]}},\"type\":{\"type\":\"string\",\"enum\":[\"branchall\"]},\"parallel\":{\"type\":\"boolean\",\"description\":\"If true, all branches execute concurrently. If false, they execute sequentially\"}},\"required\":[\"branches\",\"type\"]},\"AgentTool\":{\"type\":\"object\",\"description\":\"A tool available to an AI agent. Can be a flow module or an external MCP (Model Context Protocol) tool\",\"properties\":{\"id\":{\"type\":\"string\",\"description\":\"Unique identifier for this tool. Cannot contain spaces - use underscores instead (e.g., 'get_user_data' not 'get user data')\"},\"summary\":{\"type\":\"string\",\"description\":\"The name the AI agent calls this tool by, not a human label. On a flowmodule tool it must match ^[a-zA-Z0-9_]+$ - letters, numbers and underscores only (e.g. 'search_documentation', not 'Search documentation') - and always be set; on an mcp or websearch tool it is a plain label. Put the human-readable explanation in 'description'.\"},\"description\":{\"type\":\"string\",\"description\":\"Free-text description of the tool given to the AI to decide when and how to call it. Overrides the description auto-derived from the underlying script.\"},\"job_token_scopes\":{\"type\":\"array\",\"nullable\":true,\"items\":{\"type\":\"string\"},\"description\":\"Scopes the token of this tool's jobs is restricted to, on top of the agent step's own restriction: a tool can only narrow it.\"},\"value\":{\"$ref\":\"#/components/schemas/ToolValue\"}},\"required\":[\"id\",\"value\"]},\"ToolValue\":{\"description\":\"The implementation of a tool. Can be a flow module (script/flow) or an MCP tool reference\",\"oneOf\":[{\"$ref\":\"#/components/schemas/FlowModuleTool\"},{\"$ref\":\"#/components/schemas/McpToolValue\"},{\"$ref\":\"#/components/schemas/WebsearchToolValue\"}],\"discriminator\":{\"propertyName\":\"tool_type\",\"mapping\":{\"flowmodule\":\"#/components/schemas/FlowModuleTool\",\"mcp\":\"#/components/schemas/McpToolValue\",\"websearch\":\"#/components/schemas/WebsearchToolValue\"}}},\"FlowModuleTool\":{\"description\":\"A tool implemented as a flow module (script, flow, etc.). The AI can call this like any other flow module\",\"allOf\":[{\"type\":\"object\",\"properties\":{\"tool_type\":{\"type\":\"string\",\"enum\":[\"flowmodule\"]}},\"required\":[\"tool_type\"]},{\"$ref\":\"#/components/schemas/FlowModuleValue\"}]},\"WebsearchToolValue\":{\"type\":\"object\",\"description\":\"A tool implemented as a websearch tool. The AI can call this like any other websearch tool\",\"properties\":{\"tool_type\":{\"type\":\"string\",\"enum\":[\"websearch\"]}},\"required\":[\"tool_type\"]},\"McpToolValue\":{\"type\":\"object\",\"description\":\"Reference to an external MCP (Model Context Protocol) tool. The AI can call tools from MCP servers\",\"properties\":{\"tool_type\":{\"type\":\"string\",\"enum\":[\"mcp\"]},\"resource_path\":{\"type\":\"string\",\"description\":\"Path to the MCP resource/server configuration\"},\"include_tools\":{\"type\":\"array\",\"description\":\"Whitelist of specific tools to include from this MCP server\",\"items\":{\"type\":\"string\"}},\"exclude_tools\":{\"type\":\"array\",\"description\":\"Blacklist of tools to exclude from this MCP server\",\"items\":{\"type\":\"string\"}}},\"required\":[\"tool_type\",\"resource_path\"]},\"AiAgent\":{\"type\":\"object\",\"description\":\"AI agent step that can use tools to accomplish tasks. The agent receives inputs and can call any of its configured tools to complete the task\",\"properties\":{\"input_transforms\":{\"type\":\"object\",\"description\":\"Input parameters for the AI agent mapped to their values\",\"properties\":{\"provider\":{\"$ref\":\"#/components/schemas/ProviderTransform\"},\"output_type\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Output format type.\\nValid values: 'text' (default) - plain text response, 'image' - image generation\\n\"},\"user_message\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"The user's prompt/message to the AI agent. Supports variable interpolation with\\nflow.input syntax. Required unless memory is off and `previous_messages` supplies\\nthe prompt; image output always needs it.\\n\"},\"system_prompt\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"System instructions that guide the AI's behavior, persona, and response style. Optional.\"},\"streaming\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Boolean. If true, stream the AI response incrementally.\\nStreaming events include: token_delta, reasoning_token_delta, tool_call, tool_call_arguments, tool_execution, tool_result\\n\"},\"memory\":{\"$ref\":\"#/components/schemas/MemoryTransform\"},\"memory_id\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"String. Names the memory this step reads and writes, overriding the memory id the run\\nwas started with (the chat conversation, an app chat session or the `memory_id` run\\nparameter). Leave unset to use the run's memory id. A fixed value shares one memory\\nacross every run; an expression such as `flow_input.customer_id` keeps one memory per\\nkey. When it evaluates to an empty value the agent runs without memory. Read only\\nwhile `memory` is `window` or `compaction`: it is ignored when memory is off, and an\\nolder `auto` or `manual` memory reads neither history input.\\n\"},\"previous_messages\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Array of MemoryMessage. History supplied by the flow, sent between the system prompt\\nand the user message. Read only while `memory` is off or absent: managed memory\\nignores it, and an older `auto` or `manual` memory reads neither history input.\\n\"},\"output_schema\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"JSON Schema object defining structured output format. Used when you need the AI to return data in a specific shape.\\nSupports standard JSON Schema properties: type, properties, required, items, enum, pattern, minLength, maxLength, minimum, maximum, etc.\\nExample: { type: 'object', properties: { name: { type: 'string' }, age: { type: 'integer' } }, required: ['name'] }\\n\"},\"user_attachments\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Array of file references (images or PDFs) for the AI agent.\\nFormat: Array<{ bucket: string, key: string }> - S3 object references\\nExample: [{ bucket: 'my-bucket', key: 'documents/report.pdf' }]\\n\"},\"enabled_tools\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Array of strings naming which of the tools configured in `tools` the agent may call\\nthis run. Leaving it unset carries every one of them; an empty array carries none.\\nA tool is named as the model is shown it. An entry the model is shown nothing of is\\nnamed by what identifies it instead: an MCP server by its resource path, carrying\\nevery tool it exposes (which of them stays that entry's include_tools/exclude_tools),\\nand a websearch entry by the reserved name '__wm_web_search', whatever summary it carries\\n(no tool may take that name).\\nExample: ['get_user', 'u/admin/github_mcp', '__wm_web_search']\\n\"},\"max_completion_tokens\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Integer. Maximum number of tokens the AI will generate in its response.\\nRange: 1 to 4,294,967,295. Typical values: 256-4096 for most use cases.\\n\"},\"temperature\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Float. Controls randomness/creativity of responses.\\nRange: 0.0 to 2.0 (provider-dependent)\\n- 0.0 = deterministic, focused responses\\n- 0.7 = balanced (common default)\\n- 1.0+ = more creative/random\\n\"},\"max_iterations\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Number. Limits how many times the agent can loop through reasoning and tool use.\\nRange: 1-1000.\\n\"},\"model\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"String. Only on a step linked to a saved agent (see `agent`): replaces the model\\nsaved on the agent for this step's runs, on the agent's own provider resource. An\\nempty value keeps the saved model. Set it to an expression such as\\n`flow_input.model` to choose the model per run.\\n\"},\"reasoning_effort\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"String. Only on a step linked to a saved agent (see `agent`): replaces the reasoning\\neffort saved on the agent for this step's runs, as a provider-native token (e.g.\\n'low', 'high', 'none'). An empty value sends no effort, so the model runs at its\\ndefault. Leave the input out to keep the saved effort, which holds only while the\\nstep runs the saved model. A step that is not linked sets both inside `provider`.\\n\"}}},\"tools\":{\"type\":\"array\",\"description\":\"Array of tools the agent can use. The agent decides which tools to call based on the task\",\"items\":{\"$ref\":\"#/components/schemas/AgentTool\"}},\"type\":{\"type\":\"string\",\"enum\":[\"aiagent\"]},\"tag\":{\"type\":\"string\",\"description\":\"Worker group tag for execution routing. If not set, the AI agent step runs on the flow's tag (default `flow`)\"},\"omit_output_from_conversation\":{\"type\":\"boolean\",\"default\":false,\"description\":\"If true, this AI agent step does not persist its assistant or tool messages to the flow conversation when chat mode is enabled.\"},\"agent\":{\"type\":\"string\",\"description\":\"Path of a reusable `ai_agent` resource (hybrid linking). When set, the agent brain\\nconfig (provider/model/system prompt/etc.) and tool set are resolved at runtime from\\nthat resource; the module's input_transforms then only carry the flow-local inputs\\n(user_message, user_attachments, enabled_tools and the history inputs memory_id and previous_messages)\\nand, optionally, the `model` and `reasoning_effort` overrides of what the agent saved.\\n\"},\"tool_inputs\":{\"type\":\"object\",\"description\":\"Host-local wiring for an agent's tool inputs, keyed by tool id then input key. Binds the\\nreferenced agent's tools to this flow's context (flow_input/results) without mutating the\\nshared resource; overlaid onto the tools' input_transforms at runtime \\u2014 including when\\n`agent` is unset, since a step forked for editing keeps these overrides until it is saved\\nback or unlinked.\\n\",\"additionalProperties\":{\"type\":\"object\",\"additionalProperties\":{\"$ref\":\"#/components/schemas/InputTransform\"}}},\"parallel\":{\"type\":\"boolean\",\"description\":\"If true, the agent can execute multiple tool calls in parallel\"}},\"required\":[\"type\",\"input_transforms\"]},\"AiDecision\":{\"type\":\"object\",\"description\":\"AI decision step: one call to a decision model (TypeSafe's Jev, Cloudflare's\\nJev-compatible Clef, or OpenAI's Decisions API) that answers typed questions about a\\nstate with calibrated probabilities, instead of generating text.\\nPrefer it over an AI agent to classify, route, score or check something: it is fast,\\ncheap and its answers are structured. Its result is { output: { <question name>: answer },\\nmodel, usage }, so a later step reads e.g. results.<id>.output.intent.choice. To route on\\nthe answers, follow it with a branchone whose branch exprs read\\nresults.<id>.output.<question>.choice. Usable as an AI agent tool, where the calling agent\\nsupplies the state.\\n\",\"properties\":{\"input_transforms\":{\"type\":\"object\",\"description\":\"Input parameters for the decision mapped to their values\",\"properties\":{\"provider\":{\"$ref\":\"#/components/schemas/ProviderTransform\"},\"state\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"What the questions are asked about: a string, an object or an array of strings.\\nAn object with descriptive keys holding only what the questions need works best.\\nExample: { message: flow_input.message, plan: results.a.plan }\\n\"},\"questions\":{\"allOf\":[{\"$ref\":\"#/components/schemas/InputTransform\"}],\"description\":\"Object mapping each question name to { type, instructions, criteria }, where type is:\\n- 'choice': criteria maps each option to its description (up to 255); the answer\\n  has choice, probabilities and confidence\\n- 'score': criteria is an ordered array of 2 to 10 level descriptions; the answer\\n  has score, legend, probabilities and confidence\\n- 'noul': yes/no, criteria optionally { true, false } descriptions; the answer has\\n  noul, the probability of yes (0 to 1)\\nExample: { intent: { type: 'choice', instructions: 'What does the customer want?',\\ncriteria: { refund: 'Money back', bug: 'Something is broken' } } }\\n\"}},\"required\":[\"provider\",\"state\",\"questions\"]},\"type\":{\"type\":\"string\",\"enum\":[\"aidecision\"]},\"tag\":{\"type\":\"string\",\"description\":\"Worker group tag for execution routing. If not set, the step runs on the flow's tag (default `flow`)\"}},\"required\":[\"type\",\"input_transforms\"]},\"Identity\":{\"type\":\"object\",\"description\":\"Pass-through module that returns its input unchanged. Useful for flow structure or as a placeholder\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"identity\"]},\"flow\":{\"type\":\"boolean\",\"description\":\"If true, marks this as a flow identity (special handling)\"}},\"required\":[\"type\"]},\"FlowStatus\":{\"type\":\"object\",\"properties\":{\"step\":{\"type\":\"integer\"},\"modules\":{\"type\":\"array\",\"items\":{\"$ref\":\"#/components/schemas/FlowStatusModule\"}},\"user_states\":{\"additionalProperties\":true},\"preprocessor_module\":{\"allOf\":[{\"$ref\":\"#/components/schemas/FlowStatusModule\"}]},\"failure_module\":{\"allOf\":[{\"$ref\":\"#/components/schemas/FlowStatusModule\"},{\"type\":\"object\",\"properties\":{\"parent_module\":{\"type\":\"string\"}}}]},\"retry\":{\"type\":\"object\",\"properties\":{\"fail_count\":{\"type\":\"integer\"},\"failed_jobs\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"format\":\"uuid\"}}}}},\"required\":[\"step\",\"modules\",\"failure_module\"]},\"FlowStatusModule\":{\"type\":\"object\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"WaitingForPriorSteps\",\"WaitingForEvents\",\"WaitingForExecutor\",\"InProgress\",\"Success\",\"Failure\"]},\"id\":{\"type\":\"string\"},\"job\":{\"type\":\"string\",\"format\":\"uuid\"},\"count\":{\"type\":\"integer\"},\"progress\":{\"type\":\"integer\"},\"iterator\":{\"type\":\"object\",\"properties\":{\"index\":{\"type\":\"integer\"},\"itered\":{\"type\":\"array\",\"items\":{}},\"itered_len\":{\"type\":\"integer\"},\"args\":{}}},\"flow_jobs\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}},\"flow_jobs_success\":{\"type\":\"array\",\"items\":{\"type\":\"boolean\"}},\"flow_jobs_duration\":{\"type\":\"object\",\"properties\":{\"started_at\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}},\"duration_ms\":{\"type\":\"array\",\"items\":{\"type\":\"integer\"}}}},\"branch_chosen\":{\"type\":\"object\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"branch\",\"default\"]},\"branch\":{\"type\":\"integer\"}},\"required\":[\"type\"]},\"branchall\":{\"type\":\"object\",\"properties\":{\"branch\":{\"type\":\"integer\"},\"len\":{\"type\":\"integer\"}},\"required\":[\"branch\",\"len\"]},\"approvers\":{\"type\":\"array\",\"items\":{\"type\":\"object\",\"properties\":{\"resume_id\":{\"type\":\"integer\"},\"approver\":{\"type\":\"string\"}},\"required\":[\"resume_id\",\"approver\"]}},\"failed_retries\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"format\":\"uuid\"}},\"skipped\":{\"type\":\"boolean\"},\"agent_actions\":{\"type\":\"array\",\"items\":{\"type\":\"object\",\"oneOf\":[{\"type\":\"object\",\"properties\":{\"job_id\":{\"type\":\"string\",\"format\":\"uuid\"},\"function_name\":{\"type\":\"string\"},\"type\":{\"type\":\"string\",\"enum\":[\"tool_call\"]},\"module_id\":{\"type\":\"string\"}},\"required\":[\"job_id\",\"function_name\",\"type\",\"module_id\"]},{\"type\":\"object\",\"properties\":{\"call_id\":{\"type\":\"string\",\"format\":\"uuid\"},\"function_name\":{\"type\":\"string\"},\"resource_path\":{\"type\":\"string\"},\"type\":{\"type\":\"string\",\"enum\":[\"mcp_tool_call\"]},\"arguments\":{\"type\":\"object\"}},\"required\":[\"call_id\",\"function_name\",\"resource_path\",\"type\"]},{\"type\":\"object\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"web_search\"]}},\"required\":[\"type\"]},{\"type\":\"object\",\"properties\":{\"type\":{\"type\":\"string\",\"enum\":[\"message\"]}},\"required\":[\"content\",\"type\"]}]}},\"agent_actions_success\":{\"type\":\"array\",\"items\":{\"type\":\"boolean\"}}},\"required\":[\"type\"]}}",
		"resource": "# Global draft resource & variable instructions\n\n- Global mode writes complete draft payloads only; it does not save, deploy, run, scaffold local files, or generate metadata.\n- A resource draft is a workspace item: `{ type: 'resource', path, summary?, value, isDraft }`. `value` is a CreateResource body: `{ path, value, description?, resource_type, labels? }` where the inner `value` is the resource type's data shape.\n- Reading a variable returns `{ type: 'variable', path, summary?, isSecret, isDraft }` — never its value, secret or not. `isSecret` tells you whether the value is encrypted.\n- `write_variable` takes `{ path, value?, is_secret?, description?, account?, is_oauth?, expires_at?, labels? }`. Creating a variable needs `value` and `is_secret`; editing one needs only the fields you are changing. Omitting `value` keeps the stored value, which is the only way to edit a secret variable — you cannot read its value, so passing any `value` you did not get from the user destroys it.\n- For secret fields in a resource value, create the variable with `write_variable` and `is_secret: true`, and deploy it before the resource (see \"Secrets\" in the reference below).\n- Reference formats inside resource values: `$var:g/all/name` (global), `$var:u/user/name` (user), `$var:f/folder/name` (folder). Reference another resource with `$res:path/to/resource`. The same strings are also how a resource or variable is passed as a run argument (see the run-argument rule in the resource reference below); what they are never valid as is a variable's own value.\n- Use `search_resource_types` to discover valid `resource_type` names and their JSON Schemas. Match the resource value to that schema.\n- For OAuth resources, the `is_oauth: true` flag is managed by Windmill's OAuth flow; global mode generally creates manual resources, not OAuth ones.\n\n# Windmill resource & variable reference\n\n# Windmill Resources\n\nResources store credentials and configuration for external services.\n\n## Resource Structure\n\n```json\n{\n  \"value\": {\n    \"host\": \"db.example.com\",\n    \"port\": 5432,\n    \"user\": \"admin\",\n    \"password\": \"$var:g/all/db_password\",\n    \"dbname\": \"production\"\n  },\n  \"description\": \"Production PostgreSQL database\",\n  \"resource_type\": \"postgresql\"\n}\n```\n\n## Required Fields\n\n- `value` - Object containing the resource configuration\n- `resource_type` - Name of the resource type (e.g., \"postgresql\", \"slack\")\n\n## Agents\n\nAn agent, as the Agents page lists it, is a resource of type `ai_agent`. Its value holds the\nagent's provider, system prompt, tools and memory settings, and a flow step uses a saved agent by\nlinking its path.\n\n## Variable References\n\nReference variables in resource values:\n\n```json\n{\n  \"value\": {\n    \"api_key\": \"$var:g/all/api_key\",\n    \"secret\": \"$var:u/admin/secret\"\n  }\n}\n```\n\n**Reference formats:**\n- `$var:g/all/name` - Global variable\n- `$var:u/username/name` - User variable\n- `$var:f/folder/name` - Folder variable\n\n## Secrets\n\nNever put a secret (password, API key, token) inline in a resource value. Store it in a secret variable and reference that variable as `$var:<path>`.\n\n- `$var:<path>` is a reference, not a value: it resolves to the variable's value at run time. Never invent a value for a variable.\n- A resource that references a variable needs the variable to exist first, so create or deploy the variable before the resource.\n\n## Resource References\n\nReference other resources:\n\n```json\n{\n  \"value\": {\n    \"database\": \"$res:f/databases/postgres\"\n  }\n}\n```\n\n## Passing a Resource or Variable as a Run Argument\n\nA script or flow argument typed as a resource (schema `format: resource-<type>`) is passed as\nthe **bare string** `$res:<path>` — the whole argument value. Same for a variable, with\n`$var:<path>`. This applies everywhere job arguments are supplied: `wmill script run/preview`,\n`wmill flow run/preview`, the `runScriptByPath` / `runFlowByPath` API, a schedule's `args`, a\ntrigger's configured static args.\n\n```json\n{\n  \"db\": \"$res:f/databases/postgres_prod\",\n  \"api_token\": \"$var:g/all/api_token\"\n}\n```\n\nThe reference is resolved when the job runs, under the job's run-as identity — the caller for an\nordinary run, but the configured principal for a schedule, a trigger, or a runnable set to run on\nbehalf of someone else. The run fails if that identity cannot read the referenced resource or\nvariable.\n\n**Never wrap it in an object.** The resolver only rewrites a JSON value that *is* a string\nstarting with `$res:` / `$var:`; keys are never inspected. These are all wrong and are passed\nthrough to the script unchanged:\n\n```json\n{ \"db\": { \"$res\": \"f/databases/postgres_prod\" } }\n{ \"db\": { \"resource\": \"f/databases/postgres_prod\" } }\n{ \"db\": \"f/databases/postgres_prod\" }\n```\n\nThe string may sit anywhere a string can — a top-level argument, a nested object field\n(`{ \"gh_auth\": { \"token\": \"$var:g/all/gh_token\" } }`), or an array element (array elements are\nwalked only while nested at most two levels deep, and only for arrays of at most 1000 items).\nThe prefix must be on the string itself.\n\n## Common Resource Types\n\n### PostgreSQL\n```json\n{\n  \"resource_type\": \"postgresql\",\n  \"value\": {\n    \"host\": \"localhost\",\n    \"port\": 5432,\n    \"user\": \"postgres\",\n    \"password\": \"$var:g/all/pg_password\",\n    \"dbname\": \"windmill\",\n    \"sslmode\": \"prefer\"\n  }\n}\n```\n\n### MySQL\n```json\n{\n  \"resource_type\": \"mysql\",\n  \"value\": {\n    \"host\": \"localhost\",\n    \"port\": 3306,\n    \"user\": \"root\",\n    \"password\": \"$var:g/all/mysql_password\",\n    \"database\": \"myapp\"\n  }\n}\n```\n\n### Slack\n```json\n{\n  \"resource_type\": \"slack\",\n  \"value\": {\n    \"token\": \"$var:g/all/slack_token\"\n  }\n}\n```\n\n### AWS S3\n```json\n{\n  \"resource_type\": \"s3\",\n  \"value\": {\n    \"bucket\": \"my-bucket\",\n    \"region\": \"us-east-1\",\n    \"accessKeyId\": \"$var:g/all/aws_access_key\",\n    \"secretAccessKey\": \"$var:g/all/aws_secret_key\"\n  }\n}\n```\n\n### HTTP/API\n```json\n{\n  \"resource_type\": \"http\",\n  \"value\": {\n    \"baseUrl\": \"https://api.example.com\",\n    \"headers\": {\n      \"Authorization\": \"Bearer $var:g/all/api_token\"\n    }\n  }\n}\n```\n\n### Kafka\n```json\n{\n  \"resource_type\": \"kafka\",\n  \"value\": {\n    \"brokers\": \"broker1:9092,broker2:9092\",\n    \"sasl_mechanism\": \"PLAIN\",\n    \"security_protocol\": \"SASL_SSL\",\n    \"username\": \"$var:g/all/kafka_user\",\n    \"password\": \"$var:g/all/kafka_password\"\n  }\n}\n```\n\n### NATS\n```json\n{\n  \"resource_type\": \"nats\",\n  \"value\": {\n    \"servers\": [\"nats://localhost:4222\"],\n    \"user\": \"$var:g/all/nats_user\",\n    \"password\": \"$var:g/all/nats_password\"\n  }\n}\n```\n\n### MQTT\n```json\n{\n  \"resource_type\": \"mqtt\",\n  \"value\": {\n    \"host\": \"mqtt.example.com\",\n    \"port\": 8883,\n    \"username\": \"$var:g/all/mqtt_user\",\n    \"password\": \"$var:g/all/mqtt_password\",\n    \"tls\": true\n  }\n}\n```\n\n## When No Resource Type Fits\n\nThe Windmill Hub publishes resource types for most services, and an instance gets them by syncing with the hub. When none fits the service:\n\n- If the instance is reported as not having synced the hub's types, mention that a superadmin can sync them: the hub most likely has one for the service. That holds for a type a hub script takes too: it is missing here until the sync, so say so before asking for a resource of it. Create one of your own only if the user still wants it, since after a sync the workspace would hold both.\n- Otherwise, or when the user prefers it to syncing, propose a custom resource type named `c_<name>` holding the service's connection fields (base URL, account or region identifiers, credentials), as in \"Custom Resource Types\" below, and ask the user before creating it: every resource of that type and every script taking one depends on its fields.\n- A plain variable instead of a resource is fine when the user asks for it; without that, default to the resource, its secret in a variable it references.\n\n## Custom Resource Types\n\nA custom type's name starts with `c_`: the hub's types reach every instance under their plain names, so the prefix keeps a custom type from clashing with one synced later. Scripts and flows refer to the full name: `format: resource-c_custom_api`, `RT.CCustomApi` in TypeScript, a `c_custom_api` TypedDict in Python.\n\nCreate custom resource types with JSON Schema:\n\n```json\n{\n  \"name\": \"c_custom_api\",\n  \"schema\": {\n    \"type\": \"object\",\n    \"properties\": {\n      \"base_url\": {\"type\": \"string\", \"format\": \"uri\"},\n      \"api_key\": {\"type\": \"string\"},\n      \"timeout\": {\"type\": \"integer\", \"default\": 30}\n    },\n    \"required\": [\"base_url\", \"api_key\"]\n  },\n  \"description\": \"Custom API connection\"\n}\n```\n\nSave as: `c_custom_api.resource-type.json`\n\n## OAuth Resources\n\nOAuth resources are managed through the Windmill UI and marked:\n\n```json\n{\n  \"is_oauth\": true,\n  \"account\": 123\n}\n```\n\nOAuth tokens are automatically refreshed by Windmill.\n\n## Using Resources in Scripts\n\n### TypeScript (Bun/Deno)\n```typescript\nexport async function main(db: RT.Postgresql) {\n  // db contains the resource values\n  const { host, port, user, password, dbname } = db;\n}\n```\n\n### Python\n```python\nclass postgresql(TypedDict):\n    host: str\n    port: int\n    user: str\n    password: str\n    dbname: str\n\ndef main(db: postgresql):\n    # db contains the resource values\n    pass\n```\n\n",
		"app": "# Global draft app instructions\n\n- Global mode edits raw app drafts only; it does not save or deploy unless the user explicitly asks to deploy.\n- App drafts are addressed by workspace path. Follow the path conventions in the system prompt: default to `u/<current-user>/<name>` for bare names; only use `f/<folder>/<name>` when the folder is known to exist. The first write tool snapshots the workspace app onto the draft, and subsequent writes accumulate.\n- To create a new app, use `init_app` with a path, optional summary, a framework (`react19` / `react18` / `svelte5`) and, for an app that stores data, its `data` setup. \"Starting a new app\" below decides the framework and when the data setup is asked. Ask the user each of its questions, suggested answer first, before `init_app` and before any SQL. `init_app` only records `data`: a new schema is yours to create with the datatable SQL tool. `init_app` errors if an app already exists at the path or a draft is already in flight; in that case, edit the existing one rather than re-initializing.\n- `init_app` seeds the starter inline runnables `greet`, `word_stats`, `slow_task`, `stream_text` so the demo tour works on first render. Replace or remove them once you start building real backend runnables.\n- Frontend file paths start with `/` (e.g. `/index.tsx`, `/App.tsx`, `/styles.css`). Use `write_app_file` / `patch_app_file` / `delete_app_file`.\n- Backend inline runnables are addressed as `backend/<key>/main.{ts|py}` from the file tools, but you create or update them via `write_app_runnable` / `delete_app_runnable` (which take the runnable shape directly: `{ name, type, inlineScript?, path?, staticInputs? }`).\n- `/wmill.d.ts` (or `wmill.ts`) is generated automatically from the backend runnables — never write it directly.\n- Inline runnables only support `bun` or `python3` in chat. Path runnables (`script`/`flow`/`hubscript`) reference an existing item.\n- When a path runnable points at a draft (see \"Draft code vs deployed code\" below), offer to deploy just that flow/script with deploy_workspace_item, not through the compare page.\n- The authoring reference below carries the TypeScript SDK. For a `python3` runnable, call `get_instructions` again with `subject: \"app\"` and `language: \"python3\"`.\n- Use `deploy_workspace_item` after explicit user deploy intent. The deploy tool bundles JS/CSS before saving the raw app.\n- Use `read_workspace_item` with `type: 'app'` for a metadata summary (file paths and runnable list, no contents). Use `read_app_file` to read an individual file; large files are truncated to a head slice, so pass `offset`/`limit` to page through the rest rather than re-reading the whole file.\n- To find where a symbol or string lives across the app, call `search_app` (greps every frontend file and inline runnable, returns matching `file:line` rows) instead of reading files one by one — then `read_app_file` only the ranges you need. The loop is list (`read_workspace_item`) → locate (`search_app`) → inspect (`read_app_file` with `offset`/`limit`).\n\n# Windmill raw app authoring reference\n\n# Windmill Raw Apps\n\nRaw apps let you build custom frontends with React or Svelte that connect to Windmill backend runnables and datatables.\n\n## App shape\n\nA raw app has three logical parts:\n\n- **Frontend** — bundled with esbuild from `index.tsx` as the entrypoint. Files include the entrypoint, components (`App.tsx`), styles, etc.\n- **Backend runnables** — server-side scripts the frontend calls, each addressed by a unique key.\n- **Data** — optional whitelisted datatables (managed PostgreSQL) that the backend runnables can query. The frontend never queries the database directly; backend runnables are the only bridge.\n\n## Starting a new app\n\nA new app gets the setup Windmill's own new-app dialog gives one: a framework, then where its data lives.\n\n### Framework\n\nUse the framework the user named. When they named none, follow what there is to follow: the app being edited, or the apps the request points at. With nothing to follow, use **React 19**, the recommended default, **without asking**. React 18 and Svelte 5 are for when they are asked for. These three are the only frameworks the raw app bundler builds. When the user asks to build or convert an app in another framework, write no code in it: say it is not available and offer these three instead.\n\n### Data setup\n\nWhen a new app has to store data and the user did not say where, **ask before creating anything**: no app, no schema, no table. Where the data lives is the user's decision, and tables created in the wrong place stay there. Look up the workspace's data tables first so every choice you offer exists, then ask, as proposed answers rather than open questions:\n\n1. **Data table**: which one to use, the default first (`main` when it exists, otherwise the first listed). Skip this one when the workspace has a single data table.\n2. **Schema**: where the app's new tables go.\n   - A **new schema**, the usual choice, offered first. Suggest the first unused name among `app1`, `app2`, …\n   - An **existing schema** of that data table, by name.\n   - **None**: the tables go in `public`.\n3. **Tables**: only when that data table already holds tables the app could use, whether to **reuse** them (name them) or **create new ones**.\n\nDo not ask when:\n\n- the user already said where the data goes. A part they left out takes its default above: the default data table, a new `appN` schema, new tables;\n- the app, or its saved draft, already carries a data config (`data.datatable`, `data.schema` or `data.tables`). That config is the answer;\n- the app stores nothing.\n\nWith no data table in the workspace there is nothing to choose: say that one has to be configured in the workspace settings before the app can store data.\n\nA question left unanswered or dismissed is not a go-ahead for the defaults: stop there and say what is still needed.\n\nThen build what was chosen: the data table and schema become the app's `data.datatable` and `data.schema`, the tables it uses, reused or new, go in `data.tables`, and a new schema is created (`CREATE SCHEMA IF NOT EXISTS`) before the tables that go in it.\n\n### An app that arrives already set up\n\nAn app the user just started from the new-app dialog is already set up: its files are the framework template it was started with, and a `data` config it carries (`data.datatable`, `data.schema` or `data.tables`) is the data setup it was started with. Build that app in place, with that data config, and in that framework unless the user now names another. Do not ask for either, and do not create a second app. Only when it carries no data config and has to store data is the data setup still to ask.\n\n## Frontend\n\n### Entrypoint\n\nThe entrypoint is `index.tsx` for React and `index.ts` for Svelte. It is both the bundling entrypoint (the bundler is esbuild) and the **mount** entrypoint: the preview executes the bundle against an empty `<div id=\"root\">` and auto-renders nothing, so the entrypoint must mount a top-level `App` itself. Keep the UI in `App.tsx` / `App.svelte` and keep the entrypoint as the mount shim.\n\nReact (`index.tsx`):\n\n```tsx\nimport React from 'react'\nimport { createRoot } from 'react-dom/client'\nimport App from './App'\n\ncreateRoot(document.getElementById('root')!).render(<App />)\n```\n\nSvelte (`index.ts`): `mount(App, { target: document.getElementById('root')! })`.\n\n**Never replace the entrypoint with a bare component** (`export default function App() { ... }` and no mount call). A component that is defined but never mounted renders a blank screen with **no error thrown** — it never executes, so nothing reaches the console or the error overlay. If an app renders blank, check that the entrypoint still mounts `App` into `#root`.\n\n**Set the browser tab title with `document.title = '...'`** (or a `<title>` the framework renders into `<head>`). The app has no `index.html` of its own, so a `<title>` written there is never served; the deployed app's page shows whatever `document.title` holds, and follows it when it changes.\n\n**Always begin every React file (`.tsx`/`.jsx`) that uses JSX with `import React from 'react'`.** esbuild uses the classic JSX transform, so `React` must be in scope wherever JSX appears — a missing import compiles fine but throws `React is not defined` at runtime, leaving a blank screen.\n\n### Generated bindings (`wmill.d.ts` / `wmill.ts`)\n\nThe frontend imports a generated module that mirrors the backend runnables. **Never write to it directly** — it gets regenerated whenever backend runnables change. Modifying it by hand will be overwritten.\n\n### Calling backend runnables\n\nImport the generated bindings and call the runnable like a function. `./wmill` is the **only** way the frontend reaches anything server-side — datatables, workspace items, external services. Never `fetch` the Windmill API from frontend code: the bundle holds no token and builds no API URL.\n\n| Export | Resolves to | Use it for |\n|---|---|---|\n| `backend.<key>(args)` | the runnable's result | the default — run and wait |\n| `backendAsync.<key>(args)` | the **job id** (a string) | long-running work you want to track |\n| `waitJob(jobId)` | the job's **result** (rejects if the job failed) | awaiting a `backendAsync` job |\n| `getJob(jobId)` | a `Job` (`{ type, success, result, duration_ms, ... }`) | polling status without blocking |\n| `streamJob(jobId, onUpdate?)` | the final result, calling `onUpdate` per chunk | showing output as it is produced |\n\nA runnable is always called with **one object** whose keys are its `main` parameters — `main(user_id: string, limit: number)` is called as `backend.get_users({ user_id, limit })`, never with positional arguments. A runnable without parameters is called with no argument. Resource and variable ids handed to the `wmill` client are paths (`u/<user>/<name>` or `f/<folder>/<name>`).\n\nRun and wait — the common case:\n\n```tsx\nimport { backend } from './wmill';\n\nconst user = await backend.get_user({ user_id: '123' });\n```\n\nStart a long job, then await it:\n\n```tsx\nimport { backendAsync, waitJob } from './wmill';\n\nconst jobId = await backendAsync.run_report({ month: '2026-08' }); // a string\nconst report = await waitJob(jobId);                               // the result itself\n```\n\nOr poll it without blocking, to render progress:\n\n```tsx\nimport { getJob } from './wmill';\n\nconst job = await getJob(jobId);\nif (job.type === 'CompletedJob') setReport(job.result);\n```\n\n`backendAsync` resolves a job id and nothing else — guard on it before storing or polling. A poll loop started on an `undefined` id never completes and shows as a row stuck \"running\" forever:\n\n```tsx\nconst jobId = await backendAsync.run_report(args);\nif (!jobId) throw new Error('run_report did not start a job');\n```\n\n**Never hand-write a job-polling runnable.** A backend runnable that calls `jobs/list`, or that returns `getResultMaybe(...)` for the frontend to poll, reimplements `backendAsync` + `waitJob` / `getJob` / `streamJob` — and it is what leads to guessing at base URLs and tokens.\n\n### Keeping data out of recorded demos\n\nAn app can be demoed by recording a session: every interaction becomes a step carrying a snapshot of the page, replayed publicly or on the Hub. Password inputs are masked automatically. Mark anything else that must not appear with `data-wm-no-record` — the whole marked subtree is dropped from every snapshot, along with its values and the step's own metadata:\n\n```tsx\n<label data-wm-no-record>\n  Customer SSN <input value={ssn} onChange={onSsn} />\n</label>\n```\n\nApply it to customer data, internal notes and anything else a viewer of the demo should not see. It costs nothing when the app is never recorded.\n\n### Chat UIs over a flow in chat mode\n\nA flow deployed with chat mode on is a chat backend (streaming answer, tool calls, memory, conversation history). Do not drive it through a runnable: add `@windmill-labs/chat-sdk` to `package.json` and use it directly, it detects the app's Windmill and credential.\n\n```tsx\nimport { useWindmillChat } from '@windmill-labs/chat-sdk/react'\n\nconst chat = useWindmillChat({ flowPath: 'f/support/assistant' })\n// chat.messages ({ role, content, pending, success, tool? }), chat.status, chat.sendMessage(text), chat.stop()\n```\n\n`@windmill-labs/chat-sdk/ai-sdk` gives a `ChatTransport` for the Vercel AI SDK's `useChat`, `@windmill-labs/chat-sdk/assistant-ui` a runtime for assistant-ui. The flow must be deployed, not a draft. A sandboxed app needs `jobs:run` in its frontend SDK scopes, plus `flow_conversations:write` for the conversation sidebar; without them the chat keeps history in the browser.\n\n## Backend runnables\n\nEach runnable has a unique key (used to call it from the frontend) and one of four types:\n\n| Type | What it is |\n|---|---|\n| `inline` | Custom code stored on the app itself. Most common for app-specific logic. |\n| `script` | Reference to an existing workspace script by path. |\n| `flow` | Reference to an existing workspace flow by path. |\n| `hubscript` | Reference to a hub script by path. |\n\n### Inline runnables\n\nInline runnables carry their own source code, and must expose a `main` function as their entrypoint.\n\n**TypeScript example** (runnable `get_user`):\n\n```typescript\nimport * as wmill from 'windmill-client';\n\nexport async function main(user_id: string) {\n  const sql = wmill.datatable();\n  const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();\n  return user;\n}\n```\n\n**Python example** (runnable `get_user`):\n\n```python\nimport wmill\n\ndef main(user_id: str):\n    db = wmill.datatable()\n    user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()\n    return user\n```\n\n#### Sharing code between runnables\n\nAn inline runnable can import another inline runnable of the same app by its id, as a relative import: `import { getDb } from './shared'` (TypeScript, Bun) or `from .shared import get_db` (Python). This works across TypeScript (Bun) runnables and across Python runnables, not between the two. The imported runnable's own dependencies are picked up, and it can import further runnables in turn. A runnable that only holds helpers needs no `main`; the frontend just never calls it. A TypeScript runnable whose id is `main` cannot be imported, because that name is taken by the entrypoint file of every TypeScript job: give shared code any other id.\n\n#### The `wmill` client is already authenticated\n\nAn inline runnable runs as an ordinary Windmill job. `import * as wmill from 'windmill-client'` (TypeScript) and `import wmill` (Python) are already pointed at this instance and this workspace — there is nothing to configure.\n\n**Don't read `WM_TOKEN` or `BASE_INTERNAL_URL` and build an API URL to `fetch`.** The client's own `setClient` already reads exactly those, and it also sets the credentials mode a raw app needs (`WM_RAW_APP` suppresses credentials, because a sandboxed bundle calls the API from an opaque origin that can never pair with `Access-Control-Allow-Origin: *`). Rebuilding that by hand drops the parts you can't see. Use `wmill.*` for everything Windmill, and `fetch` only for third-party APIs.\n\nUse only `wmill` functions the SDK actually exports; for an endpoint none of them covers, the generated service classes (`JobService`, `ScriptService`, ...) are importable from `windmill-client`. What is not available is a name you guessed at: `getBaseUrl` and `getWorkspaceToken` are inventions, not API.\n\n### Path runnables (script / flow / hubscript)\n\nWhen `type` is `script`, `flow`, or `hubscript`, the runnable just stores a `path` to an existing workspace or hub item — no inline code. The referenced item's input/output schema becomes the runnable's surface.\n\nBefore writing an inline runnable, look for a workspace script or flow that already does the job, or a Hub script (a prebuilt integration at `hub/<version>/<app>/<name>`) for a third-party service, and reference it instead of copying its logic.\n\n### Draft code vs deployed code\n\nThis decides whether an app works before anything is deployed:\n\n- **Inline runnables run the app's current code.** The editor sends the runnable's source with each request, so an inline runnable works in the preview with nothing deployed.\n- **Path runnables (`script` / `flow` / `hubscript`) run the DEPLOYED item at that path.** So do `wmill.runFlow`, `wmill.runFlowAsync` and `wmill.runScriptByPath` called from inside a runnable. A draft — including a draft you just created — does not exist for them.\n\nSo an app wired to a flow you just wrote does nothing until **that flow is deployed**. The app itself does NOT have to be deployed for this: the preview runs the app's draft, so the referenced flow is the only thing that has to exist deployed.\n\nThat makes the fix a one-item deploy, not a release. Offer to deploy exactly the referenced flow or script and leave the app a draft the user keeps testing in the preview — do not push the whole change set through the review-and-deploy page, and do not ask the user to deploy the app, unless they said they want to ship it.\n\nDo NOT quietly reimplement the flow inside an inline runnable to dodge the deployment: that leaves the user with two copies of the same logic and an app that ignores the flow they asked for. Inline the logic only when the user actually wants it inline.\n\nPrefer a **path runnable of type `flow`** over an inline runnable that calls `wmill.runFlowAsync`. The path runnable gives the frontend the flow's real input schema and works with `backend` / `backendAsync` / `waitJob` like any other runnable; a hand-written wrapper gives up all of that.\n\n### Static inputs\n\n`staticInputs` is an optional `Record<string, any>` for arguments not overridable from the frontend. Useful with path runnables to pre-fill some args while leaving the rest to the frontend caller.\n\n## Who can open a deployed app\n\nA draft app is reachable by nobody; deploying is what exposes it, and its backend runnables with it. Otherwise only users with access to the app can open it, unless the app is opened to:\n\n- **anonymous** users: anyone with the URL, without logging in;\n- **guests**: anyone the instance's identity provider authenticates, whether or not they belong to the workspace.\n\nEither one lets those people run the app's backend runnables, so never open an app up unless the user asks.\nWhen a deploy widens who may open the app, tell the user in plain words.\n\n## Sandbox isolation and frontend API access\n\nNew apps are sandboxed: the frontend is isolated from the viewer's own Windmill session and reaches Windmill only through its backend runnables, plus whatever frontend API scopes the app declares, which each viewer approves before the app runs. With isolation turned off, the frontend runs with the viewer's full session. The scopes are `jobs:run`, `jobs:read`, `resources:read`, `variables:read`, `flow_conversations:read` and `flow_conversations:write`; grant only the ones the frontend calls. The viewer's identity needs no scope: it is in `window.ctx`, and whoami works with any of them.\n\nInside the sandbox, IndexedDB is unavailable, third-party sign-in and OAuth redirects fail, and requests to other services carry `Origin: null`, which an API that checks the origin rejects. Work around these first: call the service from a backend runnable, keep state in a data table or `localStorage`, or grant the frontend API scope the code needs. Suggest turning isolation off only when none of that works, and say plainly that it is a security risk: the app's code, and any XSS bug in it, then runs with the full Windmill permissions of everyone who opens the app.\nYou cannot turn isolation off; the user does it in the app's deploy settings.\n\n## Reading data as the viewer\n\nA backend runnable reads data as the app's identity (the publisher in publisher mode), the same for every viewer. An inline runnable can instead read data as the person using the app: its data table connections that name no role hold that viewer's roles, and object storage permission rules (which paths may be listed, read, written) are checked against the viewer. Resources, variables and everything else stay the app's, and an anonymous viewer is refused. Turn it on only when what the runnable reads should differ per viewer: row-level security on a data table, or per-user paths in object storage. Both are Enterprise Edition features.\nSet it with `viewerDataAccess: true` on the backend runnable.\n\n## Data Tables\n\nData tables are PostgreSQL databases managed by Windmill. Backend runnables query them via the `wmill` client; the frontend never queries them directly. **When the app needs to store or persist data** (user data, settings, application state, records, logs), use a data table.\n\n### Critical rules\n\n1. **Check what exists first**: look up the workspace's data tables and their tables before designing storage, and reuse a suitable table rather than creating another. Never assume a `main` data table exists.\n2. **Whitelisted tables only**: a runnable can only query tables listed in the app's `data.tables` config. Queries against unlisted tables fail at runtime, so register a new table there before using it.\n3. **No DDL inside runnables**: runnables only read and write rows (SELECT, INSERT, UPDATE, DELETE) on existing tables. Never CREATE, ALTER or DROP a table from a runnable.\n4. **Qualify table names**: an unqualified name means the `public` schema, so write every other table as `schema.table`, in table creation and queries alike. The app's `data` config sets the default datatable and schema its tables go in; use them consistently across runnables.\n5. **Pass the role**: when the app's `data.roles` gives a data table a role, every `wmill.datatable` call on it passes that role (`wmill.datatable('main', { role: 'analyst' })` in TypeScript, `wmill.datatable('main', role='analyst')` in Python). The role only reaches what it was granted, so a query outside it fails with `permission denied`.\n6. **Per-viewer rows**: a data table with no entry in `data.roles` is called with no role. Unless its default role is `admin`, the connection then holds the default role, if it may use it, plus every role it is a tenant of, and is refused when that is none. Whose roles those are follows the runnable's identity (see \"Reading data as the viewer\"). Row-level security policies written as `pg_has_role(current_user, '<role>', 'member')` then return the right rows with nothing added to the code. A role pins the connection to that one role either way, so a data table whose rows should differ per viewer gets no `data.roles` entry. Data table roles are an Enterprise Edition feature, self-hosted only.\n\n### Querying in TypeScript (Bun/Deno)\n\n```typescript\nimport * as wmill from 'windmill-client';\n\nexport async function main(user_id: string) {\n  const sql = wmill.datatable();  // Or: wmill.datatable('other_datatable')\n\n  // Parameterized queries (safe from SQL injection)\n  const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();\n  const users = await sql`SELECT * FROM users WHERE active = ${true}`.fetch();\n\n  // Insert/Update\n  await sql`INSERT INTO users (name, email) VALUES (${name}, ${email})`.execute();\n  await sql`UPDATE users SET name = ${newName} WHERE id = ${user_id}`.execute();\n\n  return user;\n}\n```\n\n### Querying in Python\n\n```python\nimport wmill\n\ndef main(user_id: str):\n    db = wmill.datatable()  # Or: wmill.datatable('other_datatable')\n\n    # Use $1, $2, etc. for parameters\n    user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()\n    users = db.query('SELECT * FROM users WHERE active = $1', True).fetch()\n\n    # Insert/Update\n    db.query('INSERT INTO users (name, email) VALUES ($1, $2)', name, email).execute()\n    db.query('UPDATE users SET name = $1 WHERE id = $2', new_name, user_id).execute()\n\n    return user\n```\n\n## Best Practices\n\n1. **Check existing tables** before creating new ones — reuse beats schema growth.\n2. **Use parameterized queries** — never concatenate user input into SQL.\n3. **Terminate every datatable statement** — the tagged template and `db.query(...)` only build a statement. It runs when you call `fetch` / `fetchOne` / `fetchOneScalar` / `execute` (`fetch` / `fetch_one` / `fetch_one_scalar` / `execute` in Python). An INSERT or UPDATE without one writes nothing and raises nothing. Awaiting the statement itself is a no-op — it is not a promise.\n4. **Keep runnables focused** — one function per runnable; small surface area.\n5. **Use descriptive keys** — `get_user`, not `a`.\n6. **Always whitelist tables** — adding a runnable that queries a new table requires the table to be in `data.tables` first.\n7. **Mark sensitive UI with `data-wm-no-record`** — it is what keeps that data out of a recorded demo; passwords are handled for you.\n8. **Reach for `backendAsync` + `waitJob`** for long work — never a hand-written job-polling runnable.\n9. **Deploy what a path runnable points at** — a path runnable aimed at a draft fails at runtime; tell the user what needs deploying.\n10. **Use `@windmill-labs/chat-sdk` for a chat over a chat-mode flow** — never a runnable that runs the flow and polls its stream.\n\n\n# TypeScript SDK (windmill-client)\n\nImport: import * as wmill from 'windmill-client'\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe helpers below are the surface to prefer. For an endpoint none of them covers, import the\ngenerated service classes (JobService, ScriptService, ...) from 'windmill-client' — they are not\nlisted here but they do exist. What does not exist is a helper name you guessed at: if it is\nneither listed below nor a service method, do not call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`process.env.WM_END_USER_EMAIL || process.env.WM_EMAIL`. WM_END_USER_EMAIL is the app viewer when\nthe run was triggered from an app and empty otherwise (both variables are always defined), WM_EMAIL\nis the user the job is permissioned as. WM_USERNAME is the matching username.\n\nworkerHasInternalServer(): boolean\n\n/**\n * Initialize the Windmill client with authentication token and base URL\n * @param token - Authentication token (defaults to WM_TOKEN env variable)\n * @param baseUrl - API base URL (defaults to BASE_INTERNAL_URL or BASE_URL env variable)\n */\nsetClient(token?: string, baseUrl?: string): void\n\n/**\n * Create a client configuration from env variables\n * @returns client configuration\n */\ngetWorkspace(): string\n\n/**\n * Get a resource value by path\n * @param path path of the resource,  default to internal state path\n * @param undefinedIfEmpty if the resource does not exist, return undefined instead of throwing an error\n * @returns resource value\n */\nasync getResource(path?: string, undefinedIfEmpty?: boolean): Promise<any>\n\n/**\n * Get the true root job id\n * @param jobId job id to get the root job id from (default to current job)\n * @returns root job id\n */\nasync getRootJobId(jobId?: string): Promise<string>\n\n/**\n * Run a script synchronously by its path and wait for the result\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByPath(path: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Run a script synchronously by its hash and wait for the result\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Script execution result\n */\nasync runScriptByHash(hash_: string, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Append a text to the result stream\n * @param text text to append to the result stream\n */\nappendToResultStream(text: string): void\n\n/**\n * Stream to the result stream\n * @param stream stream to stream to the result stream\n */\nasync streamResult(stream: AsyncIterable<string>): Promise<void>\n\n/**\n * Run a flow synchronously by its path and wait for the result\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param verbose - Enable verbose logging\n * @param tag - Override the worker tag the job runs on\n * @returns Flow execution result\n */\nasync runFlow(path: string | null = null, args: Record<string, any> | null = null, verbose: boolean = false, tag: string | null = null): Promise<any>\n\n/**\n * Wait for a job to complete and return its result\n * @param jobId - ID of the job to wait for\n * @param verbose - Enable verbose logging\n * @returns Job result when completed\n */\nasync waitJob(jobId: string, verbose: boolean = false): Promise<any>\n\n/**\n * Get the result of a completed job\n * @param jobId - ID of the completed job\n * @returns Job result\n */\nasync getResult(jobId: string): Promise<any>\n\n/**\n * Get the result of a job if completed, or its current status\n * @param jobId - ID of the job\n * @returns Object with started, completed, success, and result properties\n */\nasync getResultMaybe(jobId: string): Promise<any>\n\n/**\n * Cancel a queued or running job by ID.\n * @param jobId - UUID of the job to cancel\n * @param reason - Optional reason for cancellation\n * @returns Response message from the cancel endpoint\n */\nasync cancelJob(jobId: string, reason: string | undefined = undefined): Promise<string>\n\n/**\n * Run a script asynchronously by its path\n * @param path - Script path in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByPathAsync(path: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a script asynchronously by its hash\n * @param hash_ - Script hash in Windmill\n * @param args - Arguments to pass to the script\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds), or {@link RunScriptAsyncOptions}\n *   `{ scheduledInSeconds?, tag?, retry? }`, e.g. `{ retry: { constant: { attempts: 3, seconds: 10 } } }`\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runScriptByHashAsync(hash_: string, args: Record<string, any> | null = null, scheduledInSeconds: number | null | RunScriptAsyncOptions = null, tag: string | null = null): Promise<string>\n\n/**\n * Run a flow asynchronously by its path\n * @param path - Flow path in Windmill\n * @param args - Arguments to pass to the flow\n * @param scheduledInSeconds - Schedule execution for a future time (in seconds)\n * @param doNotTrackInParent - If false, tracks state in parent job (only use when fully awaiting the job)\n * @param tag - Override the worker tag the job runs on\n * @returns Job ID of the created job\n */\nasync runFlowAsync(path: string | null, args: Record<string, any> | null, scheduledInSeconds: number | null = null, // can only be set to false if this the job will be fully await and not concurrent with any other job // as otherwise the child flow and its own child will store their state in the parent job which will // lead to incorrectness and failures doNotTrackInParent: boolean = true, tag: string | null = null): Promise<string>\n\n/**\n * Resolve a resource value in case the default value was picked because the input payload was undefined\n * @param obj resource value or path of the resource under the format `$res:path`\n * @returns resource value\n */\nasync resolveDefaultResource(obj: any): Promise<any>\n\n/**\n * Get the state file path from environment variables\n * @returns State path string\n */\ngetStatePath(): string\n\n/**\n * Set a resource value by path\n * @param path path of the resource to set, default to state path\n * @param value new value of the resource to set\n * @param initializeToTypeIfNotExist if the resource does not exist, initialize it with this type\n */\nasync setResource(value: any, path?: string, initializeToTypeIfNotExist?: string): Promise<void>\n\n/**\n * Set the state\n * @param state state to set\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync setState(state: any, path?: string): Promise<void>\n\n/**\n * Set the progress\n * Progress cannot go back and limited to 0% to 99% range\n * @param percent Progress to set in %\n * @param jobId? Job to set progress for\n */\nasync setProgress(percent: number, jobId?: any): Promise<void>\n\n/**\n * Get the progress\n * @param jobId? Job to get progress from\n * @returns Optional clamped between 0 and 100 progress value\n */\nasync getProgress(jobId?: any): Promise<number | null>\n\n/**\n * Whether the worker running this job has received its shutdown signal.\n * A draining worker waits for its current job to finish before exiting, so a\n * long-running script can poll this and return early to let the worker be replaced.\n * @param jobId? Job to check, defaults to the current job\n */\nasync workerIsDraining(jobId?: string): Promise<boolean>\n\n/**\n * Set a flow user state\n * @param key key of the state\n * @param value value of the state\n */\nasync setFlowUserState(key: string, value: any, errorIfNotPossible?: boolean): Promise<void>\n\n/**\n * Get a flow user state\n * @param path path of the variable\n */\nasync getFlowUserState(key: string, errorIfNotPossible?: boolean): Promise<any>\n\n/**\n * Get the state shared across executions\n * @param path Optional state resource path override. Defaults to `getStatePath()`.\n */\nasync getState(path?: string): Promise<any>\n\n/**\n * Get a variable by path\n * @param path path of the variable\n * @returns variable value\n */\nasync getVariable(path: string): Promise<string>\n\n/**\n * Set a variable by path, create if not exist\n * @param path path of the variable\n * @param value value of the variable\n * @param isSecretIfNotExist if the variable does not exist, create it as secret or not (default: false)\n * @param descriptionIfNotExist if the variable does not exist, create it with this description (default: \"\")\n */\nasync setVariable(path: string, value: string, isSecretIfNotExist?: boolean, descriptionIfNotExist?: string): Promise<void>\n\n/**\n * Build a PostgreSQL connection URL from a database resource\n * @param path - Path to the database resource\n * @returns PostgreSQL connection URL string\n */\nasync databaseUrlFromResource(path: string): Promise<string>\n\nasync polarsConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\nasync duckdbConnectionSettings(s3_resource_path: string | undefined): Promise<any>\n\n/**\n * Get S3 client settings from a resource or workspace default\n * @param s3_resource_path - Path to S3 resource (uses workspace default if undefined)\n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n * @returns S3 client configuration settings\n */\nasync denoS3LightClientSettings(s3_resource_path: string | undefined, workspace: string | undefined = undefined): Promise<DenoS3LightClientSettings>\n\n/**\n * Load the content of a file stored in S3. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContent = await wmill.loadS3FileContent(inputFile)\n * // if the file is a raw text file, it can be decoded and printed directly:\n * const text = new TextDecoder().decode(fileContentStream)\n * console.log(text);\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3File(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Uint8Array | undefined>\n\n/**\n * Load the content of a file stored in S3 as a stream. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * let fileContentBlob = await wmill.loadS3FileStream(inputFile)\n * // if the content is plain text, the blob can be read directly:\n * console.log(await fileContentBlob.text());\n * ```\n * \n * @param workspace - Workspace to read from (defaults to the `WM_WORKSPACE` env var)\n */\nasync loadS3FileStream(s3object: S3Object, s3ResourcePath: string | undefined = undefined, workspace: string | undefined = undefined): Promise<Blob | undefined>\n\n/**\n * Persist a file to the S3 bucket. If the s3ResourcePath is undefined, it will default to the workspace S3 resource.\n * \n * ```typescript\n * const s3object = await writeS3File(s3Object, \"Hello Windmill!\")\n * const fileContentAsUtf8Str = (await s3object.toArray()).toString('utf-8')\n * console.log(fileContentAsUtf8Str)\n * ```\n * \n * @param workspace - Workspace to write to (defaults to the `WM_WORKSPACE` env var)\n */\nasync writeS3File(s3object: S3Object | undefined, fileContent: string | Blob, s3ResourcePath: string | undefined = undefined, contentType: string | undefined = undefined, contentDisposition: string | undefined = undefined, workspace: string | undefined = undefined): Promise<S3Object>\n\n/**\n * Permanently delete a file from S3 by key.\n * \n * ```typescript\n * await wmill.deleteS3File({ s3: \"path/to/file.txt\" })\n * ```\n * \n * @param s3object - S3 object identifying the file to delete (must have `s3` set)\n * @param workspace - Workspace to delete from (defaults to the `WM_WORKSPACE` env var)\n */\nasync deleteS3File(s3object: S3Object, workspace: string | undefined = undefined): Promise<void>\n\n/**\n * Sign S3 objects to be used by anonymous users in public apps\n * @param s3objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 objects\n */\nasync signS3Objects(s3objects: S3Object[], { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object[]>\n\n/**\n * Sign S3 object to be used by anonymous users in public apps\n * @param s3object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed s3 object\n */\nasync signS3Object(s3object: S3Object, { expirySecs }: { expirySecs?: number } = {}): Promise<S3Object>\n\n/**\n * Generate a presigned public URL for an array of S3 objects.\n * If an S3 object is not signed yet, it will be signed first.\n * @param s3Objects s3 objects to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns list of signed public URLs\n */\nasync getPresignedS3PublicUrls(s3Objects: S3Object[], { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string[]>\n\n/**\n * Generate a presigned public URL for an S3 object. If the S3 object is not signed yet, it will be signed first.\n * @param s3Object s3 object to sign\n * @param expirySecs how long the signature stays valid, in seconds (default 43200 = 12h, clamped to [60, 604800])\n * @returns signed public URL\n */\nasync getPresignedS3PublicUrl(s3Objects: S3Object, { baseUrl, expirySecs }: { baseUrl?: string; expirySecs?: number } = {}): Promise<string>\n\n/**\n * Get URLs needed for resuming a flow after this step\n * @param approver approver name\n * @param flowLevel if true, generate resume URLs for the parent flow instead of the specific step.\n *                  This allows pre-approvals that can be consumed by any later suspend step in the same flow.\n * @returns approval page UI URL, resume and cancel API URLs for resuming the flow\n */\nasync getResumeUrls(approver?: string, flowLevel?: boolean): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Get an OIDC jwt token for auth to external services (e.g: Vault, AWS) (ee only)\n * @param audience audience of the token\n * @param expiresIn Optional number of seconds until the token expires\n * @returns jwt token\n */\nasync getIdToken(audience: string, expiresIn?: number): Promise<string>\n\n/**\n * Convert a base64-encoded string to Uint8Array\n * @param data - Base64-encoded string\n * @returns Decoded Uint8Array\n */\nbase64ToUint8Array(data: string): Uint8Array\n\n/**\n * Convert a Uint8Array to base64-encoded string\n * @param arrayBuffer - Uint8Array to encode\n * @returns Base64-encoded string\n */\nuint8ArrayToBase64(arrayBuffer: Uint8Array): string\n\n/**\n * Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Slack approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Slack approval request.\n * @param {string} options.slackResourcePath - The path to the Slack resource in Windmill.\n * @param {string} options.channelId - The Slack channel ID where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Slack approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * @param {string} [options.resumeButtonText] - Optional text for the resume button.\n * @param {string} [options.cancelButtonText] - Optional text for the cancel button.\n * \n * @returns {Promise<void>} Resolves when the Slack approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getSlackApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveSlackApproval({\n *   slackResourcePath: \"/u/alex/my_slack_resource\",\n *   channelId: \"admins-slack-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n *   resumeButtonText: \"Resume\",\n *   cancelButtonText: \"Cancel\",\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveSlackApproval({ slackResourcePath, channelId, message, approver, defaultArgsJson, dynamicEnumsJson, resumeButtonText, cancelButtonText, }: SlackApprovalOptions): Promise<void>\n\n/**\n * Sends an interactive approval request via Teams, allowing optional customization of the message, approver, and form fields.\n * \n * **[Enterprise Edition Only]** To include form fields in the Teams approval request, go to **Advanced -> Suspend -> Form**\n * and define a form. Learn more at [Windmill Documentation](https://www.windmill.dev/docs/flows/flow_approval#form).\n * \n * @param {Object} options - The configuration options for the Teams approval request.\n * @param {string} options.teamName - The Teams team name where the approval request will be sent.\n * @param {string} options.channelName - The Teams channel name where the approval request will be sent.\n * @param {string} [options.message] - Optional custom message to include in the Teams approval request.\n * @param {string} [options.approver] - Optional user ID or name of the approver for the request.\n * @param {DefaultArgs} [options.defaultArgsJson] - Optional object defining or overriding the default arguments to a form field.\n * @param {Enums} [options.dynamicEnumsJson] - Optional object overriding the enum default values of an enum form field.\n * \n * @returns {Promise<void>} Resolves when the Teams approval request is successfully sent.\n * \n * @throws {Error} If the function is not called within a flow or flow preview.\n * @throws {Error} If the `JobService.getTeamsApprovalPayload` call fails.\n * \n * **Usage Example:**\n * ```typescript\n * await requestInteractiveTeamsApproval({\n *   teamName: \"admins-teams\",\n *   channelName: \"admins-teams-channel\",\n *   message: \"Please approve this request\",\n *   approver: \"approver123\",\n *   defaultArgsJson: { key1: \"value1\", key2: 42 },\n *   dynamicEnumsJson: { foo: [\"choice1\", \"choice2\"], bar: [\"optionA\", \"optionB\"] },\n * });\n * ```\n * \n * **Note:** This function requires execution within a Windmill flow or flow preview.\n */\nasync requestInteractiveTeamsApproval({ teamName, channelName, message, approver, defaultArgsJson, dynamicEnumsJson, }: TeamsApprovalOptions): Promise<void>\n\nsetWorkflowCtx(ctx: WorkflowCtx | null): void\n\nasync sleep(seconds: number): Promise<void>\n\n/**\n * Execute `fn` inline and checkpoint the result. On replay the cached value is\n * returned without re-executing `fn`.\n * \n * `fn`'s result is encoded as JSON and decoded back before it is returned, so\n * the round that runs the body sees the same types every replay sees: a `Date`\n * comes back as a string, a `Map` as `{}`. {@link Jsonified} is that shape.\n * \n * Called from inside a task body, `fn` runs in place and nothing is\n * checkpointed: a task is a single job and is never replayed.\n */\nasync step<T>(name: string, fn: () => T | Promise<T>,): Promise<Jsonified<Awaited<T>>>\n\n/**\n * Create a task that dispatches to a separate Windmill script.\n * \n * @example\n * const extract = taskScript(\"f/data/extract\");\n * // inside workflow: await extract({ url: \"https://...\" })\n */\ntaskScript(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Create a task that dispatches to a separate Windmill flow.\n * \n * @example\n * const pipeline = taskFlow(\"f/etl/pipeline\");\n * // inside workflow: await pipeline({ input: data })\n */\ntaskFlow(path: string, options?: TaskOptions): (...args: any[]) => PromiseLike<any>\n\n/**\n * Mark an async function as a workflow-as-code entry point.\n * \n * The function must be **deterministic**: given the same inputs it must call\n * tasks in the same order on every replay. Branching on task results is fine\n * (results are replayed from checkpoint), but branching on external state\n * (current time, random values, external API calls) must use `step()` to\n * checkpoint the value so replays see the same result.\n */\nworkflow<T>(fn: (...args: any[]) => Promise<T>): void\n\n/**\n * Suspend the workflow and wait for an external approval.\n * \n * Pass `key` to name the step, then `getApprovalUrls(key)` yields the URLs that\n * resume exactly this approval — route them through your own channel. Without a\n * key the steps are named `approval`, `approval_2`, ...\n * \n * `skin: \"minimal\"` shows approvers only the request (form and approve/reject)\n * instead of the detailed page with the workflow's details. `description` is\n * shown above the form: a string, or a rich value such as `{ markdown: \"...\" }`.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * const { value, approver } = await waitForApproval({ key: \"manager\", timeout: 3600 });\n */\nwaitForApproval(options?: { timeout?: number; form?: object; selfApproval?: boolean; key?: string; skin?: \"detailed\" | \"minimal\"; description?: string | object; }): PromiseLike<{ value: any; approver: string; approved: boolean }>\n\n/**\n * Resume/cancel/approval-page URLs bound to one `waitForApproval` step.\n * \n * Unlike `getResumeUrls()`, which signs a random nonce, these address the very\n * `resume_job` record the step's built-in approval buttons use, so they are\n * stable across replays and safe to embed in a custom notification.\n * \n * `stepKey` must match the `key` given to `waitForApproval`. Keys must be unique\n * within a workflow; reusing one throws rather than silently renaming it. The URL\n * only resumes while that step is awaiting approval; used at any other moment it is\n * rejected rather than banking a row a different approval would consume. Send it\n * ahead of time — approvers just cannot act before the workflow reaches the step.\n * \n * `resume` and `cancel` are step-bound; `approvalPage` is not — it opens the job's\n * approval page, which acts on whichever approval is pending when it is used.\n * \n * @example\n * const urls = await step(\"urls\", () => getApprovalUrls(\"manager\"));\n * await step(\"notify\", () => sendEmail(urls.resume, urls.cancel));\n * await waitForApproval({ key: \"manager\" });\n */\nasync getApprovalUrls(stepKey: string = \"approval\", approver?: string): Promise<{\n  approvalPage: string;\n  resume: string;\n  cancel: string;\n}>\n\n/**\n * Process items in parallel with optional concurrency control.\n * \n * Each item is processed by calling `fn(item)`, which should be a task().\n * Items are dispatched in batches of `concurrency` (default: all at once).\n * \n * @example\n * const process = task(async (item: string) => { ... });\n * const results = await parallel(items, process, { concurrency: 5 });\n */\nasync parallel<T, R>(items: T[], fn: (item: T) => PromiseLike<R> | R, options?: { concurrency?: number },): Promise<R[]>\n\n/**\n * Commit Kafka offsets for a trigger with auto_commit disabled.\n * @param triggerPath - Path to the Kafka trigger (from event.wm_trigger.trigger_path)\n * @param topic - Kafka topic name (from event.topic)\n * @param partition - Partition number (from event.partition)\n * @param offset - Message offset to commit (from event.offset)\n */\nasync commitKafkaOffsets(triggerPath: string, topic: string, partition: number, offset: number,): Promise<void>\n\n/**\n * Parse an S3 object from URI string or record format\n * @param s3Object - S3 object as URI string (`s3://storage/key`, `s3:///key`\n *   for the default storage) or record. Any other string throws rather than\n *   falling back to an auto-generated key: an auto key is requested by\n *   omitting the object, and a fallback would silently misplace the upload\n *   on any typo.\n * @returns S3 object record with storage and s3 key\n */\nparseS3Object(s3Object: S3Object): S3ObjectRecord\n\n/**\n * Create a SQL template function for PostgreSQL/datatable queries\n * @param name - Database/datatable name (default: \"main\")\n * @param opts.role - Connect as this data table role instead of the data table's default one.\n *   Only meaningful on a data table under roles, and only for a role you are a tenant of.\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.datatable()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}::int\n * `.fetch()\n * @example\n * // Read through a restricted role\n * let sql = wmill.datatable(\"main\", { role: \"analytics\" })\n */\ndatatable(name: string = \"main\", opts?: DatatableOptions): DatatableSqlTemplateFunction\n\n/**\n * Create a SQL template function for DuckDB/ducklake queries\n * @param name - DuckDB database name, optionally with a schema as `name:schema` (default: \"main\")\n * @returns SQL template function for building parameterized queries\n * @example\n * let sql = wmill.ducklake()\n * let name = 'Robin'\n * let age = 21\n * await sql`\n *   SELECT * FROM friends\n *     WHERE name = ${name} AND age = ${age}\n * `.fetch()\n * @example\n * // Target a specific schema within the ducklake\n * let sql = wmill.ducklake(\"my_lake:analytics\")\n */\nducklake(name: string = \"main\"): SqlTemplateFunction\n\n/**\n * Idempotently materialize `selectSql` into a ducklake table for one\n * partition (or the whole table when `partition` is omitted) — the client-side\n * equivalent of the `// materialize` engine.\n * With `uniqueKey` it upserts the slice (delete-by-key + insert); otherwise it\n * replaces it (whole table → `CREATE OR REPLACE`; partition → delete + insert).\n * Safe to re-run for the same partition (backfill / failure-recovery).\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.upsertPartition({ table, selectSql, partition }).execute()`.\n */\nupsertPartition(opts: DucklakeMaterializeOptions): SqlStatement<any>\n\n/**\n * INSERT-only materialization (no dedup/replace) for append-only tables.\n * Re-running the same partition duplicates rows — use only for immutable\n * event-log sources.\n * \n * Returns a lazy statement — call `.execute()` to run it:\n * `await wmill.appendPartition({ table, selectSql, partition }).execute()`.\n */\nappendPartition(opts: Omit<DucklakeMaterializeOptions, \"uniqueKey\">,): SqlStatement<any>\n",
		"app.python3": "# Global draft app instructions\n\n- Global mode edits raw app drafts only; it does not save or deploy unless the user explicitly asks to deploy.\n- App drafts are addressed by workspace path. Follow the path conventions in the system prompt: default to `u/<current-user>/<name>` for bare names; only use `f/<folder>/<name>` when the folder is known to exist. The first write tool snapshots the workspace app onto the draft, and subsequent writes accumulate.\n- To create a new app, use `init_app` with a path, optional summary, a framework (`react19` / `react18` / `svelte5`) and, for an app that stores data, its `data` setup. \"Starting a new app\" below decides the framework and when the data setup is asked. Ask the user each of its questions, suggested answer first, before `init_app` and before any SQL. `init_app` only records `data`: a new schema is yours to create with the datatable SQL tool. `init_app` errors if an app already exists at the path or a draft is already in flight; in that case, edit the existing one rather than re-initializing.\n- `init_app` seeds the starter inline runnables `greet`, `word_stats`, `slow_task`, `stream_text` so the demo tour works on first render. Replace or remove them once you start building real backend runnables.\n- Frontend file paths start with `/` (e.g. `/index.tsx`, `/App.tsx`, `/styles.css`). Use `write_app_file` / `patch_app_file` / `delete_app_file`.\n- Backend inline runnables are addressed as `backend/<key>/main.{ts|py}` from the file tools, but you create or update them via `write_app_runnable` / `delete_app_runnable` (which take the runnable shape directly: `{ name, type, inlineScript?, path?, staticInputs? }`).\n- `/wmill.d.ts` (or `wmill.ts`) is generated automatically from the backend runnables — never write it directly.\n- Inline runnables only support `bun` or `python3` in chat. Path runnables (`script`/`flow`/`hubscript`) reference an existing item.\n- When a path runnable points at a draft (see \"Draft code vs deployed code\" below), offer to deploy just that flow/script with deploy_workspace_item, not through the compare page.\n- The authoring reference below carries the Python SDK, for `python3` inline runnables. For a `bun` runnable, call `get_instructions` again with `subject: \"app\"` and no language.\n- Use `deploy_workspace_item` after explicit user deploy intent. The deploy tool bundles JS/CSS before saving the raw app.\n- Use `read_workspace_item` with `type: 'app'` for a metadata summary (file paths and runnable list, no contents). Use `read_app_file` to read an individual file; large files are truncated to a head slice, so pass `offset`/`limit` to page through the rest rather than re-reading the whole file.\n- To find where a symbol or string lives across the app, call `search_app` (greps every frontend file and inline runnable, returns matching `file:line` rows) instead of reading files one by one — then `read_app_file` only the ranges you need. The loop is list (`read_workspace_item`) → locate (`search_app`) → inspect (`read_app_file` with `offset`/`limit`).\n\n# Windmill raw app authoring reference\n\n# Windmill Raw Apps\n\nRaw apps let you build custom frontends with React or Svelte that connect to Windmill backend runnables and datatables.\n\n## App shape\n\nA raw app has three logical parts:\n\n- **Frontend** — bundled with esbuild from `index.tsx` as the entrypoint. Files include the entrypoint, components (`App.tsx`), styles, etc.\n- **Backend runnables** — server-side scripts the frontend calls, each addressed by a unique key.\n- **Data** — optional whitelisted datatables (managed PostgreSQL) that the backend runnables can query. The frontend never queries the database directly; backend runnables are the only bridge.\n\n## Starting a new app\n\nA new app gets the setup Windmill's own new-app dialog gives one: a framework, then where its data lives.\n\n### Framework\n\nUse the framework the user named. When they named none, follow what there is to follow: the app being edited, or the apps the request points at. With nothing to follow, use **React 19**, the recommended default, **without asking**. React 18 and Svelte 5 are for when they are asked for. These three are the only frameworks the raw app bundler builds. When the user asks to build or convert an app in another framework, write no code in it: say it is not available and offer these three instead.\n\n### Data setup\n\nWhen a new app has to store data and the user did not say where, **ask before creating anything**: no app, no schema, no table. Where the data lives is the user's decision, and tables created in the wrong place stay there. Look up the workspace's data tables first so every choice you offer exists, then ask, as proposed answers rather than open questions:\n\n1. **Data table**: which one to use, the default first (`main` when it exists, otherwise the first listed). Skip this one when the workspace has a single data table.\n2. **Schema**: where the app's new tables go.\n   - A **new schema**, the usual choice, offered first. Suggest the first unused name among `app1`, `app2`, …\n   - An **existing schema** of that data table, by name.\n   - **None**: the tables go in `public`.\n3. **Tables**: only when that data table already holds tables the app could use, whether to **reuse** them (name them) or **create new ones**.\n\nDo not ask when:\n\n- the user already said where the data goes. A part they left out takes its default above: the default data table, a new `appN` schema, new tables;\n- the app, or its saved draft, already carries a data config (`data.datatable`, `data.schema` or `data.tables`). That config is the answer;\n- the app stores nothing.\n\nWith no data table in the workspace there is nothing to choose: say that one has to be configured in the workspace settings before the app can store data.\n\nA question left unanswered or dismissed is not a go-ahead for the defaults: stop there and say what is still needed.\n\nThen build what was chosen: the data table and schema become the app's `data.datatable` and `data.schema`, the tables it uses, reused or new, go in `data.tables`, and a new schema is created (`CREATE SCHEMA IF NOT EXISTS`) before the tables that go in it.\n\n### An app that arrives already set up\n\nAn app the user just started from the new-app dialog is already set up: its files are the framework template it was started with, and a `data` config it carries (`data.datatable`, `data.schema` or `data.tables`) is the data setup it was started with. Build that app in place, with that data config, and in that framework unless the user now names another. Do not ask for either, and do not create a second app. Only when it carries no data config and has to store data is the data setup still to ask.\n\n## Frontend\n\n### Entrypoint\n\nThe entrypoint is `index.tsx` for React and `index.ts` for Svelte. It is both the bundling entrypoint (the bundler is esbuild) and the **mount** entrypoint: the preview executes the bundle against an empty `<div id=\"root\">` and auto-renders nothing, so the entrypoint must mount a top-level `App` itself. Keep the UI in `App.tsx` / `App.svelte` and keep the entrypoint as the mount shim.\n\nReact (`index.tsx`):\n\n```tsx\nimport React from 'react'\nimport { createRoot } from 'react-dom/client'\nimport App from './App'\n\ncreateRoot(document.getElementById('root')!).render(<App />)\n```\n\nSvelte (`index.ts`): `mount(App, { target: document.getElementById('root')! })`.\n\n**Never replace the entrypoint with a bare component** (`export default function App() { ... }` and no mount call). A component that is defined but never mounted renders a blank screen with **no error thrown** — it never executes, so nothing reaches the console or the error overlay. If an app renders blank, check that the entrypoint still mounts `App` into `#root`.\n\n**Set the browser tab title with `document.title = '...'`** (or a `<title>` the framework renders into `<head>`). The app has no `index.html` of its own, so a `<title>` written there is never served; the deployed app's page shows whatever `document.title` holds, and follows it when it changes.\n\n**Always begin every React file (`.tsx`/`.jsx`) that uses JSX with `import React from 'react'`.** esbuild uses the classic JSX transform, so `React` must be in scope wherever JSX appears — a missing import compiles fine but throws `React is not defined` at runtime, leaving a blank screen.\n\n### Generated bindings (`wmill.d.ts` / `wmill.ts`)\n\nThe frontend imports a generated module that mirrors the backend runnables. **Never write to it directly** — it gets regenerated whenever backend runnables change. Modifying it by hand will be overwritten.\n\n### Calling backend runnables\n\nImport the generated bindings and call the runnable like a function. `./wmill` is the **only** way the frontend reaches anything server-side — datatables, workspace items, external services. Never `fetch` the Windmill API from frontend code: the bundle holds no token and builds no API URL.\n\n| Export | Resolves to | Use it for |\n|---|---|---|\n| `backend.<key>(args)` | the runnable's result | the default — run and wait |\n| `backendAsync.<key>(args)` | the **job id** (a string) | long-running work you want to track |\n| `waitJob(jobId)` | the job's **result** (rejects if the job failed) | awaiting a `backendAsync` job |\n| `getJob(jobId)` | a `Job` (`{ type, success, result, duration_ms, ... }`) | polling status without blocking |\n| `streamJob(jobId, onUpdate?)` | the final result, calling `onUpdate` per chunk | showing output as it is produced |\n\nA runnable is always called with **one object** whose keys are its `main` parameters — `main(user_id: string, limit: number)` is called as `backend.get_users({ user_id, limit })`, never with positional arguments. A runnable without parameters is called with no argument. Resource and variable ids handed to the `wmill` client are paths (`u/<user>/<name>` or `f/<folder>/<name>`).\n\nRun and wait — the common case:\n\n```tsx\nimport { backend } from './wmill';\n\nconst user = await backend.get_user({ user_id: '123' });\n```\n\nStart a long job, then await it:\n\n```tsx\nimport { backendAsync, waitJob } from './wmill';\n\nconst jobId = await backendAsync.run_report({ month: '2026-08' }); // a string\nconst report = await waitJob(jobId);                               // the result itself\n```\n\nOr poll it without blocking, to render progress:\n\n```tsx\nimport { getJob } from './wmill';\n\nconst job = await getJob(jobId);\nif (job.type === 'CompletedJob') setReport(job.result);\n```\n\n`backendAsync` resolves a job id and nothing else — guard on it before storing or polling. A poll loop started on an `undefined` id never completes and shows as a row stuck \"running\" forever:\n\n```tsx\nconst jobId = await backendAsync.run_report(args);\nif (!jobId) throw new Error('run_report did not start a job');\n```\n\n**Never hand-write a job-polling runnable.** A backend runnable that calls `jobs/list`, or that returns `getResultMaybe(...)` for the frontend to poll, reimplements `backendAsync` + `waitJob` / `getJob` / `streamJob` — and it is what leads to guessing at base URLs and tokens.\n\n### Keeping data out of recorded demos\n\nAn app can be demoed by recording a session: every interaction becomes a step carrying a snapshot of the page, replayed publicly or on the Hub. Password inputs are masked automatically. Mark anything else that must not appear with `data-wm-no-record` — the whole marked subtree is dropped from every snapshot, along with its values and the step's own metadata:\n\n```tsx\n<label data-wm-no-record>\n  Customer SSN <input value={ssn} onChange={onSsn} />\n</label>\n```\n\nApply it to customer data, internal notes and anything else a viewer of the demo should not see. It costs nothing when the app is never recorded.\n\n### Chat UIs over a flow in chat mode\n\nA flow deployed with chat mode on is a chat backend (streaming answer, tool calls, memory, conversation history). Do not drive it through a runnable: add `@windmill-labs/chat-sdk` to `package.json` and use it directly, it detects the app's Windmill and credential.\n\n```tsx\nimport { useWindmillChat } from '@windmill-labs/chat-sdk/react'\n\nconst chat = useWindmillChat({ flowPath: 'f/support/assistant' })\n// chat.messages ({ role, content, pending, success, tool? }), chat.status, chat.sendMessage(text), chat.stop()\n```\n\n`@windmill-labs/chat-sdk/ai-sdk` gives a `ChatTransport` for the Vercel AI SDK's `useChat`, `@windmill-labs/chat-sdk/assistant-ui` a runtime for assistant-ui. The flow must be deployed, not a draft. A sandboxed app needs `jobs:run` in its frontend SDK scopes, plus `flow_conversations:write` for the conversation sidebar; without them the chat keeps history in the browser.\n\n## Backend runnables\n\nEach runnable has a unique key (used to call it from the frontend) and one of four types:\n\n| Type | What it is |\n|---|---|\n| `inline` | Custom code stored on the app itself. Most common for app-specific logic. |\n| `script` | Reference to an existing workspace script by path. |\n| `flow` | Reference to an existing workspace flow by path. |\n| `hubscript` | Reference to a hub script by path. |\n\n### Inline runnables\n\nInline runnables carry their own source code, and must expose a `main` function as their entrypoint.\n\n**TypeScript example** (runnable `get_user`):\n\n```typescript\nimport * as wmill from 'windmill-client';\n\nexport async function main(user_id: string) {\n  const sql = wmill.datatable();\n  const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();\n  return user;\n}\n```\n\n**Python example** (runnable `get_user`):\n\n```python\nimport wmill\n\ndef main(user_id: str):\n    db = wmill.datatable()\n    user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()\n    return user\n```\n\n#### Sharing code between runnables\n\nAn inline runnable can import another inline runnable of the same app by its id, as a relative import: `import { getDb } from './shared'` (TypeScript, Bun) or `from .shared import get_db` (Python). This works across TypeScript (Bun) runnables and across Python runnables, not between the two. The imported runnable's own dependencies are picked up, and it can import further runnables in turn. A runnable that only holds helpers needs no `main`; the frontend just never calls it. A TypeScript runnable whose id is `main` cannot be imported, because that name is taken by the entrypoint file of every TypeScript job: give shared code any other id.\n\n#### The `wmill` client is already authenticated\n\nAn inline runnable runs as an ordinary Windmill job. `import * as wmill from 'windmill-client'` (TypeScript) and `import wmill` (Python) are already pointed at this instance and this workspace — there is nothing to configure.\n\n**Don't read `WM_TOKEN` or `BASE_INTERNAL_URL` and build an API URL to `fetch`.** The client's own `setClient` already reads exactly those, and it also sets the credentials mode a raw app needs (`WM_RAW_APP` suppresses credentials, because a sandboxed bundle calls the API from an opaque origin that can never pair with `Access-Control-Allow-Origin: *`). Rebuilding that by hand drops the parts you can't see. Use `wmill.*` for everything Windmill, and `fetch` only for third-party APIs.\n\nUse only `wmill` functions the SDK actually exports; for an endpoint none of them covers, the generated service classes (`JobService`, `ScriptService`, ...) are importable from `windmill-client`. What is not available is a name you guessed at: `getBaseUrl` and `getWorkspaceToken` are inventions, not API.\n\n### Path runnables (script / flow / hubscript)\n\nWhen `type` is `script`, `flow`, or `hubscript`, the runnable just stores a `path` to an existing workspace or hub item — no inline code. The referenced item's input/output schema becomes the runnable's surface.\n\nBefore writing an inline runnable, look for a workspace script or flow that already does the job, or a Hub script (a prebuilt integration at `hub/<version>/<app>/<name>`) for a third-party service, and reference it instead of copying its logic.\n\n### Draft code vs deployed code\n\nThis decides whether an app works before anything is deployed:\n\n- **Inline runnables run the app's current code.** The editor sends the runnable's source with each request, so an inline runnable works in the preview with nothing deployed.\n- **Path runnables (`script` / `flow` / `hubscript`) run the DEPLOYED item at that path.** So do `wmill.runFlow`, `wmill.runFlowAsync` and `wmill.runScriptByPath` called from inside a runnable. A draft — including a draft you just created — does not exist for them.\n\nSo an app wired to a flow you just wrote does nothing until **that flow is deployed**. The app itself does NOT have to be deployed for this: the preview runs the app's draft, so the referenced flow is the only thing that has to exist deployed.\n\nThat makes the fix a one-item deploy, not a release. Offer to deploy exactly the referenced flow or script and leave the app a draft the user keeps testing in the preview — do not push the whole change set through the review-and-deploy page, and do not ask the user to deploy the app, unless they said they want to ship it.\n\nDo NOT quietly reimplement the flow inside an inline runnable to dodge the deployment: that leaves the user with two copies of the same logic and an app that ignores the flow they asked for. Inline the logic only when the user actually wants it inline.\n\nPrefer a **path runnable of type `flow`** over an inline runnable that calls `wmill.runFlowAsync`. The path runnable gives the frontend the flow's real input schema and works with `backend` / `backendAsync` / `waitJob` like any other runnable; a hand-written wrapper gives up all of that.\n\n### Static inputs\n\n`staticInputs` is an optional `Record<string, any>` for arguments not overridable from the frontend. Useful with path runnables to pre-fill some args while leaving the rest to the frontend caller.\n\n## Who can open a deployed app\n\nA draft app is reachable by nobody; deploying is what exposes it, and its backend runnables with it. Otherwise only users with access to the app can open it, unless the app is opened to:\n\n- **anonymous** users: anyone with the URL, without logging in;\n- **guests**: anyone the instance's identity provider authenticates, whether or not they belong to the workspace.\n\nEither one lets those people run the app's backend runnables, so never open an app up unless the user asks.\nWhen a deploy widens who may open the app, tell the user in plain words.\n\n## Sandbox isolation and frontend API access\n\nNew apps are sandboxed: the frontend is isolated from the viewer's own Windmill session and reaches Windmill only through its backend runnables, plus whatever frontend API scopes the app declares, which each viewer approves before the app runs. With isolation turned off, the frontend runs with the viewer's full session. The scopes are `jobs:run`, `jobs:read`, `resources:read`, `variables:read`, `flow_conversations:read` and `flow_conversations:write`; grant only the ones the frontend calls. The viewer's identity needs no scope: it is in `window.ctx`, and whoami works with any of them.\n\nInside the sandbox, IndexedDB is unavailable, third-party sign-in and OAuth redirects fail, and requests to other services carry `Origin: null`, which an API that checks the origin rejects. Work around these first: call the service from a backend runnable, keep state in a data table or `localStorage`, or grant the frontend API scope the code needs. Suggest turning isolation off only when none of that works, and say plainly that it is a security risk: the app's code, and any XSS bug in it, then runs with the full Windmill permissions of everyone who opens the app.\nYou cannot turn isolation off; the user does it in the app's deploy settings.\n\n## Reading data as the viewer\n\nA backend runnable reads data as the app's identity (the publisher in publisher mode), the same for every viewer. An inline runnable can instead read data as the person using the app: its data table connections that name no role hold that viewer's roles, and object storage permission rules (which paths may be listed, read, written) are checked against the viewer. Resources, variables and everything else stay the app's, and an anonymous viewer is refused. Turn it on only when what the runnable reads should differ per viewer: row-level security on a data table, or per-user paths in object storage. Both are Enterprise Edition features.\nSet it with `viewerDataAccess: true` on the backend runnable.\n\n## Data Tables\n\nData tables are PostgreSQL databases managed by Windmill. Backend runnables query them via the `wmill` client; the frontend never queries them directly. **When the app needs to store or persist data** (user data, settings, application state, records, logs), use a data table.\n\n### Critical rules\n\n1. **Check what exists first**: look up the workspace's data tables and their tables before designing storage, and reuse a suitable table rather than creating another. Never assume a `main` data table exists.\n2. **Whitelisted tables only**: a runnable can only query tables listed in the app's `data.tables` config. Queries against unlisted tables fail at runtime, so register a new table there before using it.\n3. **No DDL inside runnables**: runnables only read and write rows (SELECT, INSERT, UPDATE, DELETE) on existing tables. Never CREATE, ALTER or DROP a table from a runnable.\n4. **Qualify table names**: an unqualified name means the `public` schema, so write every other table as `schema.table`, in table creation and queries alike. The app's `data` config sets the default datatable and schema its tables go in; use them consistently across runnables.\n5. **Pass the role**: when the app's `data.roles` gives a data table a role, every `wmill.datatable` call on it passes that role (`wmill.datatable('main', { role: 'analyst' })` in TypeScript, `wmill.datatable('main', role='analyst')` in Python). The role only reaches what it was granted, so a query outside it fails with `permission denied`.\n6. **Per-viewer rows**: a data table with no entry in `data.roles` is called with no role. Unless its default role is `admin`, the connection then holds the default role, if it may use it, plus every role it is a tenant of, and is refused when that is none. Whose roles those are follows the runnable's identity (see \"Reading data as the viewer\"). Row-level security policies written as `pg_has_role(current_user, '<role>', 'member')` then return the right rows with nothing added to the code. A role pins the connection to that one role either way, so a data table whose rows should differ per viewer gets no `data.roles` entry. Data table roles are an Enterprise Edition feature, self-hosted only.\n\n### Querying in TypeScript (Bun/Deno)\n\n```typescript\nimport * as wmill from 'windmill-client';\n\nexport async function main(user_id: string) {\n  const sql = wmill.datatable();  // Or: wmill.datatable('other_datatable')\n\n  // Parameterized queries (safe from SQL injection)\n  const user = await sql`SELECT * FROM users WHERE id = ${user_id}`.fetchOne();\n  const users = await sql`SELECT * FROM users WHERE active = ${true}`.fetch();\n\n  // Insert/Update\n  await sql`INSERT INTO users (name, email) VALUES (${name}, ${email})`.execute();\n  await sql`UPDATE users SET name = ${newName} WHERE id = ${user_id}`.execute();\n\n  return user;\n}\n```\n\n### Querying in Python\n\n```python\nimport wmill\n\ndef main(user_id: str):\n    db = wmill.datatable()  # Or: wmill.datatable('other_datatable')\n\n    # Use $1, $2, etc. for parameters\n    user = db.query('SELECT * FROM users WHERE id = $1', user_id).fetch_one()\n    users = db.query('SELECT * FROM users WHERE active = $1', True).fetch()\n\n    # Insert/Update\n    db.query('INSERT INTO users (name, email) VALUES ($1, $2)', name, email).execute()\n    db.query('UPDATE users SET name = $1 WHERE id = $2', new_name, user_id).execute()\n\n    return user\n```\n\n## Best Practices\n\n1. **Check existing tables** before creating new ones — reuse beats schema growth.\n2. **Use parameterized queries** — never concatenate user input into SQL.\n3. **Terminate every datatable statement** — the tagged template and `db.query(...)` only build a statement. It runs when you call `fetch` / `fetchOne` / `fetchOneScalar` / `execute` (`fetch` / `fetch_one` / `fetch_one_scalar` / `execute` in Python). An INSERT or UPDATE without one writes nothing and raises nothing. Awaiting the statement itself is a no-op — it is not a promise.\n4. **Keep runnables focused** — one function per runnable; small surface area.\n5. **Use descriptive keys** — `get_user`, not `a`.\n6. **Always whitelist tables** — adding a runnable that queries a new table requires the table to be in `data.tables` first.\n7. **Mark sensitive UI with `data-wm-no-record`** — it is what keeps that data out of a recorded demo; passwords are handled for you.\n8. **Reach for `backendAsync` + `waitJob`** for long work — never a hand-written job-polling runnable.\n9. **Deploy what a path runnable points at** — a path runnable aimed at a draft fails at runtime; tell the user what needs deploying.\n10. **Use `@windmill-labs/chat-sdk` for a chat over a chat-mode flow** — never a runnable that runs the flow and polls its stream.\n\n\n# Python SDK (wmill)\n\nImport: import wmill\n\nThe client configures itself from the job's environment — base URL, token and credentials mode\nare all set before your code runs, so there is nothing to initialize and no reason to read\nWM_TOKEN or BASE_INTERNAL_URL and build an API URL yourself. Reconstructing that by hand only\nreintroduces details the client already handles. Call the SDK for anything Windmill, and use raw\nHTTP for third-party APIs.\n\nThe functions below are the surface to prefer. For an endpoint none of them covers,\nwmill.Windmill().get(endpoint) and .post(endpoint) issue an authenticated request against this\ninstance. What does not exist is a function name you guessed at: if it is not listed below, do\nnot call it.\n\nTo know who is running the script, read the contextual variables rather than calling the API:\n`os.environ.get(\"WM_END_USER_EMAIL\") or os.environ.get(\"WM_EMAIL\")`. WM_END_USER_EMAIL is the app\nviewer when the run was triggered from an app and empty otherwise (both variables are always\ndefined), WM_EMAIL is the user the job is permissioned as. WM_USERNAME is the matching username.\n\ndef worker_has_internal_server() -> bool\n\ndef get_mocked_api() -> Optional[dict]\n\n# Get the HTTP client instance.\n# \n# Returns:\n#     Configured httpx.Client for API requests\ndef get_client() -> httpx.Client\n\n# Make an HTTP GET request to the Windmill API.\n# \n# Args:\n#     endpoint: API endpoint path\n#     raise_for_status: Whether to raise an exception on HTTP errors\n#     **kwargs: Additional arguments passed to httpx.get\n# \n# Returns:\n#     HTTP response object\ndef get(endpoint, raise_for_status = True, **kwargs) -> httpx.Response\n\n# Make an HTTP POST request to the Windmill API.\n# \n# Args:\n#     endpoint: API endpoint path\n#     raise_for_status: Whether to raise an exception on HTTP errors\n#     **kwargs: Additional arguments passed to httpx.post\n# \n# Returns:\n#     HTTP response object\ndef post(endpoint, raise_for_status = True, **kwargs) -> httpx.Response\n\n# Create a new authentication token.\n# \n# Args:\n#     duration: Token validity duration (default: 1 day)\n# \n# Returns:\n#     New authentication token string\ndef create_token(duration = dt.timedelta(days=1)) -> str\n\n# Create a script job by path and return its job id.\n# \n# ``retry`` re-runs the job when it fails, with the policy a schedule takes,\n# e.g. ``{\"constant\": {\"attempts\": 3, \"seconds\": 10}}``. The returned id is\n# the first attempt's; each retry is a job of its own, recorded as a child of it.\ndef run_script_by_path_async(path: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None, retry: dict = None) -> str\n\n# Create a script job by hash and return its job id. See ``run_script_by_path_async`` for ``retry``.\ndef run_script_by_hash_async(hash_: str, args: dict = None, scheduled_in_secs: int = None, tag: str = None, retry: dict = None) -> str\n\n# Create a flow job and return its job id.\ndef run_flow_async(path: str, args: dict = None, scheduled_in_secs: int = None, do_not_track_in_parent: bool = True, tag: str = None) -> str\n\n# Run script by path synchronously and return its result.\ndef run_script_by_path(path: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any\n\n# Run script by hash synchronously and return its result.\ndef run_script_by_hash(hash_: str, args: dict = None, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False, tag: str = None) -> Any\n\n# Run a script on the current worker without creating a job.\n# \n# On agent workers (no internal server), falls back to running a normal\n# preview job and waiting for the result.\ndef run_inline_script_preview(content: str, language: str, args: dict = None) -> Any\n\n# Wait for a job to complete and return its result.\n# \n# Args:\n#     job_id: ID of the job to wait for\n#     timeout: Maximum time to wait (seconds or timedelta)\n#     verbose: Enable verbose logging\n#     cleanup: Register cleanup handler to cancel job on exit\n#     assert_result_is_not_none: Raise exception if result is None\n# \n# Returns:\n#     Job result when completed\n# \n# Raises:\n#     TimeoutError: If timeout is reached\n#     Exception: If job fails\ndef wait_job(job_id, timeout: dt.timedelta | int | float | None = None, verbose: bool = False, cleanup: bool = True, assert_result_is_not_none: bool = False)\n\n# Cancel a specific job by ID.\n# \n# Args:\n#     job_id: UUID of the job to cancel\n#     reason: Optional reason for cancellation\n# \n# Returns:\n#     Response message from the cancel endpoint\ndef cancel_job(job_id: str, reason: str = None) -> str\n\n# Cancel currently running executions of the same script.\ndef cancel_running() -> dict\n\n# Get job details by ID.\n# \n# Args:\n#     job_id: UUID of the job\n# \n# Returns:\n#     Job details dictionary\ndef get_job(job_id: str) -> dict\n\n# Get the root job ID for a flow hierarchy.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     Root job ID\ndef get_root_job_id(job_id: str | None = None) -> dict\n\n# Get an OIDC JWT token for authentication to external services.\n# \n# Args:\n#     audience: Token audience (e.g., \"vault\", \"aws\")\n#     expires_in: Optional expiration time in seconds\n# \n# Returns:\n#     JWT token string\ndef get_id_token(audience: str, expires_in: int | None = None) -> str\n\n# Get the status of a job.\n# \n# Args:\n#     job_id: UUID of the job\n# \n# Returns:\n#     Job status: \"RUNNING\", \"WAITING\", or \"COMPLETED\"\ndef get_job_status(job_id: str) -> JobStatus\n\n# Get the result of a completed job.\n# \n# Args:\n#     job_id: UUID of the completed job\n#     assert_result_is_not_none: Raise exception if result is None\n# \n# Returns:\n#     Job result\ndef get_result(job_id: str, assert_result_is_not_none: bool = True) -> Any\n\n# Get a variable value by path.\n# \n# Args:\n#     path: Variable path in Windmill\n# \n# Returns:\n#     Variable value as string\ndef get_variable(path: str) -> str\n\n# Set a variable value by path, creating it if it doesn't exist.\n# \n# Args:\n#     path: Variable path in Windmill\n#     value: Variable value to set\n#     is_secret: Whether the variable should be secret (default: False)\ndef set_variable(path: str, value: str, is_secret: bool = False) -> None\n\n# Get a resource value by path.\n# \n# Args:\n#     path: Resource path in Windmill\n#     none_if_undefined: Return None instead of raising if not found\n#     interpolated: if variables and resources are fully unrolled\n# \n# Returns:\n#     Resource value dictionary or None\ndef get_resource(path: str, none_if_undefined: bool = False, interpolated: bool = True) -> dict | None\n\n# Set a resource value by path, creating it if it doesn't exist.\n# \n# Args:\n#     value: Resource value to set\n#     path: Resource path in Windmill\n#     resource_type: Resource type for creation\ndef set_resource(value: Any, path: str, resource_type: str)\n\n# List resources from Windmill workspace.\n# \n# Args:\n#     resource_type: Optional resource type to filter by (e.g., \"postgresql\", \"mysql\", \"s3\")\n#     page: Optional page number for pagination\n#     per_page: Optional number of results per page\n#     \n# Returns:\n#     List of resource dictionaries\ndef list_resources(resource_type: str = None, page: int = None, per_page: int = None) -> list[dict]\n\n# Set the workflow state.\n# \n# Args:\n#     value: State value to set\n#     path: Optional state resource path override.\ndef set_state(value: Any, path: str | None = None) -> None\n\n# Get the workflow state.\n# \n# Args:\n#     path: Optional state resource path override.\n# \n# Returns:\n#     State value or None if not set\ndef get_state(path: str | None = None) -> Any\n\n# Set job progress percentage (0-99).\n# \n# Args:\n#     value: Progress percentage\n#     job_id: Job ID (defaults to current WM_JOB_ID)\ndef set_progress(value: int, job_id: Optional[str] = None)\n\n# Get job progress percentage.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     Progress value (0-100) or None if not set\ndef get_progress(job_id: Optional[str] = None) -> Any\n\n# Whether the worker running the job has received its shutdown signal.\n# \n# A draining worker waits for its current job to finish before exiting. A long-running\n# script can poll this and return early so the worker can be replaced.\n# \n# Args:\n#     job_id: Job ID (defaults to current WM_JOB_ID)\n# \n# Returns:\n#     True if the worker running the job is draining\ndef worker_is_draining(job_id: Optional[str] = None) -> bool\n\n# Set the user state of a flow at a given key\ndef set_flow_user_state(key: str, value: Any) -> None\n\n# Get the user state of a flow at a given key\ndef get_flow_user_state(key: str) -> Any\n\n# Get the Windmill server version.\n# \n# Returns:\n#     Version string\ndef version()\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from DuckDB.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: address it as ``s3://<bucket>/<key>`` with the\n# storage's own bucket, returned as ``s3_bucket``. Pass the resource path to connect with\n# the storage's own credentials instead.\ndef get_duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings | None\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from Polars.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: address it as ``s3://<bucket>/<key>`` with the\n# storage's own bucket, returned as ``s3_bucket``. Pass the resource path to connect with\n# the storage's own credentials instead.\ndef get_polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection using boto3.\n# \n# Without a resource path, the connection goes to the workspace storage through Windmill's\n# S3 proxy when that storage is S3: pass the storage's own bucket as ``Bucket``. The proxy\n# serves object reads, writes, deletes, listing and multipart uploads of that bucket only;\n# presigned URLs, other buckets and bucket-level APIs need the storage's own credentials,\n# which passing the resource path returns.\ndef get_boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings\n\n# Load a file from the workspace s3 bucket and returns its content as bytes.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# my_obj_content = client.load_s3_file(s3_obj)\n# file_content = my_obj_content.decode(\"utf-8\")\n# '''\ndef load_s3_file(s3object: S3Object | str, s3_resource_path: str | None) -> bytes\n\n# Load a file from the workspace s3 bucket and returns the bytes stream.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# with wmill.load_s3_file_reader(s3object, s3_resource_path) as file_reader:\n#     print(file_reader.read())\n# '''\ndef load_s3_file_reader(s3object: S3Object | str, s3_resource_path: str | None) -> BufferedReader\n\n# Write a file to the workspace S3 bucket\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# \n# # for an in memory bytes array:\n# file_content = b'Hello Windmill!'\n# client.write_s3_file(s3_obj, file_content)\n# \n# # for a file:\n# with open(\"my_file.txt\", \"rb\") as my_file:\n#     client.write_s3_file(s3_obj, my_file)\n# '''\ndef write_s3_file(s3object: S3Object | str | None, file_content: BufferedReader | bytes, s3_resource_path: str | None, content_type: str | None = None, content_disposition: str | None = None) -> S3Object\n\n# Permanently delete a file from the workspace S3 bucket.\n# \n# '''python\n# from wmill import S3Object\n# \n# s3_obj = S3Object(s3=\"/path/to/my_file.txt\")\n# client.delete_s3_object(s3_obj)\n# '''\ndef delete_s3_object(s3object: S3Object | str, s3_resource_path: str | None = None) -> None\n\n# Sign S3 objects for use by anonymous users in public apps.\n# \n# Args:\n#     s3_objects: List of S3 objects to sign\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     List of signed S3 objects\ndef sign_s3_objects(s3_objects: list[S3Object | str], expiry_secs: int | None = None) -> list[S3Object]\n\n# Sign a single S3 object for use by anonymous users in public apps.\n# \n# Args:\n#     s3_object: S3 object to sign\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     Signed S3 object\ndef sign_s3_object(s3_object: S3Object | str, expiry_secs: int | None = None) -> S3Object\n\n# Generate presigned public URLs for an array of S3 objects.\n# If an S3 object is not signed yet, it will be signed first.\n# \n# Args:\n#     s3_objects: List of S3 objects to sign\n#     base_url: Optional base URL for the presigned URLs (defaults to WM_BASE_URL)\n#     expiry_secs: How long the signatures stay valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     List of signed public URLs\n# \n# Example:\n#     >>> s3_objs = [S3Object(s3=\"/path/to/file1.txt\"), S3Object(s3=\"/path/to/file2.txt\")]\n#     >>> urls = client.get_presigned_s3_public_urls(s3_objs)\ndef get_presigned_s3_public_urls(s3_objects: list[S3Object | str], base_url: str | None = None, expiry_secs: int | None = None) -> list[str]\n\n# Generate a presigned public URL for an S3 object.\n# If the S3 object is not signed yet, it will be signed first.\n# \n# Args:\n#     s3_object: S3 object to sign\n#     base_url: Optional base URL for the presigned URL (defaults to WM_BASE_URL)\n#     expiry_secs: How long the signature stays valid, in seconds\n#         (defaults to 43200 = 12h, clamped to [60, 604800])\n# \n# Returns:\n#     Signed public URL\n# \n# Example:\n#     >>> s3_obj = S3Object(s3=\"/path/to/file.txt\")\n#     >>> url = client.get_presigned_s3_public_url(s3_obj)\ndef get_presigned_s3_public_url(s3_object: S3Object | str, base_url: str | None = None, expiry_secs: int | None = None) -> str\n\n# Get the current user information.\n# \n# Returns:\n#     User details dictionary\ndef whoami() -> dict\n\n# Get the current user information (alias for whoami).\n# \n# Returns:\n#     User details dictionary\ndef user() -> dict\n\n# Get the state resource path from environment.\n# \n# Returns:\n#     State path string\ndef state_path() -> str\n\n# Get the workflow state.\n# \n# Returns:\n#     State value or None if not set\ndef state() -> Any\n\n# Set the state in the shared folder using pickle\ndef set_shared_state_pickle(value: Any, path: str = 'state.pickle') -> None\n\n# Get the state in the shared folder using pickle\ndef get_shared_state_pickle(path: str = 'state.pickle') -> Any\n\n# Set the state in the shared folder using pickle\ndef set_shared_state(value: Any, path: str = 'state.json') -> None\n\n# Get the state in the shared folder using pickle\ndef get_shared_state(path: str = 'state.json') -> None\n\n# Get URLs needed for resuming a flow after suspension.\n# \n# Args:\n#     approver: Optional approver name\n#     flow_level: If True, generate resume URLs for the parent flow instead of the\n#         specific step. This allows pre-approvals that can be consumed by any later\n#         suspend step in the same flow.\n# \n# Returns:\n#     Dictionary with approvalPage, resume, and cancel URLs\ndef get_resume_urls(approver: str = None, flow_level: bool = None) -> dict\n\n# Get the resume URLs bound to one ``wait_for_approval`` step of this workflow.\n# \n# Args:\n#     step_key: Checkpoint key of the approval step, as passed to\n#         ``wait_for_approval(key=...)``\n#     approver: Optional approver name\n# \n# Returns:\n#     Dictionary with approvalPage, resume, and cancel URLs\ndef get_approval_urls(step_key: str = 'approval', approver: str = None) -> dict\n\n# Sends an interactive approval request via Slack, allowing optional customization of the message, approver, and form fields.\n# \n# **[Enterprise Edition Only]** To include form fields in the Slack approval request, use the \"Advanced -> Suspend -> Form\" functionality.\n# Learn more at: https://www.windmill.dev/docs/flows/flow_approval#form\n# \n# :param slack_resource_path: The path to the Slack resource in Windmill.\n# :type slack_resource_path: str\n# :param channel_id: The Slack channel ID where the approval request will be sent.\n# :type channel_id: str\n# :param message: Optional custom message to include in the Slack approval request.\n# :type message: str, optional\n# :param approver: Optional user ID or name of the approver for the request.\n# :type approver: str, optional\n# :param default_args_json: Optional dictionary defining or overriding the default arguments for form fields.\n# :type default_args_json: dict, optional\n# :param dynamic_enums_json: Optional dictionary overriding the enum default values of enum form fields.\n# :type dynamic_enums_json: dict, optional\n# \n# :raises Exception: If the function is not called within a flow or flow preview.\n# :raises Exception: If the required flow job or flow step environment variables are not set.\n# \n# :return: None\n# \n# **Usage Example:**\n#     >>> client.request_interactive_slack_approval(\n#     ...     slack_resource_path=\"/u/alex/my_slack_resource\",\n#     ...     channel_id=\"admins-slack-channel\",\n#     ...     message=\"Please approve this request\",\n#     ...     approver=\"approver123\",\n#     ...     default_args_json={\"key1\": \"value1\", \"key2\": 42},\n#     ...     dynamic_enums_json={\"foo\": [\"choice1\", \"choice2\"], \"bar\": [\"optionA\", \"optionB\"]},\n#     ... )\n# \n# **Notes:**\n# - This function must be executed within a Windmill flow or flow preview.\n# - The function checks for required environment variables (`WM_FLOW_JOB_ID`, `WM_FLOW_STEP_ID`) to ensure it is run in the appropriate context.\ndef request_interactive_slack_approval(slack_resource_path: str, channel_id: str, message: str = None, approver: str = None, default_args_json: dict = None, dynamic_enums_json: dict = None) -> None\n\n# Send a message to a Microsoft Teams conversation with conversation_id, where success is used to style the message\ndef send_teams_message(conversation_id: str, text: str, success: bool = True, card_block: dict = None)\n\n# Get a DataTable client for SQL queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n#     role: Connect as this data table role instead of the data table's default one.\n#         Only meaningful on a data table under roles, and only for a role you are a\n#         tenant of.\n# \n# Returns:\n#     DataTableClient instance\ndef datatable(name: str = 'main', *, role: Optional[str] = None)\n\n# Get a DuckLake client for DuckDB queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n# \n# Returns:\n#     DucklakeClient instance\ndef ducklake(name: str = 'main')\n\ndef init_global_client(f)\n\ndef deprecate(in_favor_of: str)\n\n# Get the current workspace ID.\n# \n# Returns:\n#     Workspace ID string\ndef get_workspace() -> str\n\ndef get_version() -> str\n\n# Create a script job and return its job ID.\n# \n# Args:\n#     hash_or_path: Script hash or path (determined by presence of '/')\n#     args: Script arguments\n#     scheduled_in_secs: Delay before execution in seconds\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Job ID string\ndef run_script_async(hash_or_path: str, args: Dict[str, Any] = None, scheduled_in_secs: int = None, tag: str = None) -> str\n\n# Run a script synchronously by hash and return its result.\n# \n# Args:\n#     hash: Script hash\n#     args: Script arguments\n#     verbose: Enable verbose logging\n#     assert_result_is_not_none: Raise exception if result is None\n#     cleanup: Register cleanup handler to cancel job on exit\n#     timeout: Maximum time to wait\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Script result\ndef run_script_sync(hash: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any\n\n# Run a script synchronously by path and return its result.\n# \n# Args:\n#     path: Script path\n#     args: Script arguments\n#     verbose: Enable verbose logging\n#     assert_result_is_not_none: Raise exception if result is None\n#     cleanup: Register cleanup handler to cancel job on exit\n#     timeout: Maximum time to wait\n#     tag: Override the worker tag the job runs on\n# \n# Returns:\n#     Script result\ndef run_script_by_path_sync(path: str, args: Dict[str, Any] = None, verbose: bool = False, assert_result_is_not_none: bool = True, cleanup: bool = True, timeout: dt.timedelta = None, tag: str = None) -> Any\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from DuckDB\ndef duckdb_connection_settings(s3_resource_path: str = '') -> DuckDbConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection from Polars\ndef polars_connection_settings(s3_resource_path: str = '') -> PolarsConnectionSettings\n\n# Convenient helpers that takes an S3 resource as input and returns the settings necessary to\n# initiate an S3 connection using boto3\ndef boto3_connection_settings(s3_resource_path: str = '') -> Boto3ConnectionSettings\n\n# Get the state resource path from environment.\n# \n# Returns:\n#     State path string\ndef get_state_path() -> str\n\n# Parse resource syntax from string.\ndef parse_resource_syntax(s: str) -> Optional[str]\n\n# Parse S3 object from a `s3://<storage>/<key>` URI string (`s3:///<key>`\n# for the default storage) or S3Object format. Any other string raises\n# rather than falling back to an auto-generated key: an auto key is\n# requested by omitting the object, and a fallback would silently misplace\n# the upload on any typo.\ndef parse_s3_object(s3_object: S3Object | str) -> S3Object\n\n# Parse variable syntax from string.\ndef parse_variable_syntax(s: str) -> Optional[str]\n\n# Append a text to the result stream.\n# \n# Args:\n#     text: text to append to the result stream\ndef append_to_result_stream(text: str) -> None\n\n# Stream to the result stream.\n# \n# Args:\n#     stream: stream to stream to the result stream\ndef stream_result(stream) -> None\n\n# Execute a SQL query against the DataTable.\n# \n# Args:\n#     sql: SQL query string with $1, $2, etc. placeholders\n#     *args: Positional arguments to bind to query placeholders\n# \n# Returns:\n#     SqlQuery instance for fetching results\ndef query(sql: str, *args) -> SqlQuery\n\n# Idempotently materialize the rows of `select_sql` into ducklake\n# `table` for one `partition` (or the whole table when `partition` is\n# None). Client-side equivalent of the `// materialize` engine: with\n# `unique_key` it upserts within the slice (delete-by-key + insert);\n# without it, it replaces (whole table → CREATE OR REPLACE; partition →\n# delete the partition + insert). Re-running the same slice is safe — the\n# backfill / failure-recovery contract.\n# \n# The partition value is bound as a DuckDB arg (never string-interpolated)\n# so it cannot inject SQL. `select_sql` is trusted (your own query).\ndef upsert_partition(table: str, select_sql: str, partition: str = None, unique_key: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# INSERT-only materialization (no dedup / no replace) for an immutable\n# event-log table — for one `partition`, or the whole table when\n# `partition` is None. NOTE: unlike `upsert_partition`, re-running the same\n# slice duplicates rows — use only for append-only sources.\ndef append_partition(table: str, select_sql: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# Read a materialized ducklake table, optionally a single partition.\ndef read(table: str, partition: str = None, partition_col: str = '_wm_partition', schema: str = None)\n\n# Execute query and fetch results.\n# \n# Args:\n#     result_collection: Optional result collection mode\n# \n# Returns:\n#     Query results\ndef fetch(result_collection: str | None = None)\n\n# Execute query and fetch first row of results.\n# \n# Returns:\n#     First row of query results\ndef fetch_one()\n\n# Execute query and fetch first row of results. Return result as a scalar value.\n# \n# Returns:\n#     First row of query result as a scalar value\ndef fetch_one_scalar()\n\n# Execute query and don't return any results.\n#         \ndef execute()\n\n# DuckDB executor requires explicit argument types at declaration\n# These types exist in both DuckDB and Postgres\n# Check that the types exist if you plan to extend this function for other SQL engines.\ndef infer_sql_type(value) -> str\n\ndef parse_sql_client_name(name: str) -> tuple[str, Optional[str]]\n\n# Decorator that marks a function as a workflow task.\n# \n# Works in both WAC v1 (sync, HTTP-based dispatch) and WAC v2\n# (async, checkpoint/replay) modes:\n# \n# - **v2 (inside @workflow)**: dispatches as a checkpoint step.\n# - **v1 (WM_JOB_ID set, no @workflow)**: dispatches via HTTP API.\n# - **Standalone, or called from inside another v2 task**: executes the\n#   function body directly. A task runs as a single job, so the tasks it\n#   calls run in that same job, without their own options.\n# \n# A task runs as its own job, so its result is always encoded as JSON and\n# decoded back before the caller sees it: a ``datetime`` comes back as a\n# string, a tuple as a list.\n# \n# ``retry`` re-dispatches the task after a failure, inside ``@workflow`` only.\n# Every attempt is a step of its own (``call_api``, ``call_api#2``, ...) and\n# the wait between two of them is a durable sleep, so a retrying task holds no\n# worker while it backs off. Keys: ``attempts`` (retries after the first\n# failure, a whole number from 0 to 100), ``delay`` (seconds before the first\n# retry, sub-second delays dropped), ``multiplier`` (applied to the delay\n# after each attempt, 1 keeps it constant), ``max_delay`` (ceiling in\n# seconds). ``attempts`` is required, and an out-of-range or unknown key is\n# rejected where the policy is written.\n# \n# A workflow sleeps once per round, so tasks backing off in the same fan-out\n# wait one after another rather than together: the delay before a fan-out\n# retries is the sum of every backoff pending in it, not the longest one, and\n# it grows with both the width of the fan-out and ``attempts``. Retries with\n# no ``delay`` all go out in a single round.\n# \n# ``cache_ttl`` serves a previous result of the task for that many seconds\n# instead of running it again. A task is keyed on its step key (its name and\n# call order) and the workflow's input, not on the arguments it is called\n# with, so cache one only when whether it runs, and what it receives, follow\n# from the workflow's input alone. A ``task_script`` target is keyed on the\n# arguments it is called with. It has no effect on a ``task_flow`` target,\n# which keeps its flow's own cache policy.\n# \n# Usage::\n# \n#     @task\n#     async def extract_data(url: str): ...\n# \n#     @task(path=\"f/external_script\", timeout=600, tag=\"gpu\")\n#     async def run_external(x: int): ...\n# \n#     @task(retry={\"attempts\": 3, \"delay\": 30, \"multiplier\": 2})\n#     async def call_api(payload: dict): ...\ndef task(_func = None, *, path: Optional[str] = None, tag: Optional[str] = None, timeout: Optional[int] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Create a task that dispatches to a separate Windmill script.\n# \n# ``retry`` takes the same policy as :func:`task`.\n# \n# Usage::\n# \n#     extract = task_script(\"f/data/extract\", timeout=600)\n# \n#     @workflow\n#     async def main():\n#         data = await extract(url=\"https://...\")\ndef task_script(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Create a task that dispatches to a separate Windmill flow.\n# \n# ``retry`` takes the same policy as :func:`task`.\n# \n# Usage::\n# \n#     pipeline = task_flow(\"f/etl/pipeline\", priority=10)\n# \n#     @workflow\n#     async def main():\n#         result = await pipeline(input=data)\ndef task_flow(path: str, *, timeout: Optional[int] = None, tag: Optional[str] = None, cache_ttl: Optional[int] = None, priority: Optional[int] = None, concurrency_limit: Optional[int] = None, concurrency_key: Optional[str] = None, concurrency_time_window_s: Optional[int] = None, retry: Optional[dict] = None)\n\n# Decorator marking an async function as a workflow-as-code entry point.\n# \n# The function must be **deterministic**: given the same inputs it must call\n# tasks in the same order on every replay. Branching on task results is fine\n# (results are replayed from checkpoint), but branching on external state\n# (current time, random values, external API calls) must use ``step()`` to\n# checkpoint the value so replays see the same result.\ndef workflow(func)\n\n# Execute ``fn`` inline and checkpoint the result.\n# \n# On replay the cached value is returned without re-executing ``fn``.\n# Use for lightweight deterministic operations (timestamps, random IDs,\n# config reads) that should not incur the overhead of a child job.\n# \n# ``fn``'s result is encoded as JSON and decoded back before it is returned,\n# so the round that runs the body sees the same types every replay sees:\n# a ``datetime`` comes back as a string, a tuple as a list.\n# \n# Called from inside a task body, ``fn`` runs in place and nothing is\n# checkpointed: a task is a single job and is never replayed.\nasync def step(name: str, fn)\n\n# Server-side sleep — suspend the workflow for the given duration without holding a worker.\n# \n# Inside a @workflow, the parent job suspends and auto-resumes after ``seconds``.\n# Outside a workflow, or inside a task body, falls back to ``asyncio.sleep``.\nasync def sleep(seconds: int)\n\n# Suspend the workflow and wait for an external approval.\n# \n# Pass ``key`` to name the step, then ``get_approval_urls(key)`` yields the URLs\n# that resume exactly this approval — route them through your own channel.\n# Without a key the steps are named ``approval``, ``approval_2``, ...\n# \n# Returns a dict with ``value`` (form data), ``approver``, and ``approved``.\n# \n# Args:\n#     timeout: Approval timeout in seconds (default 1800).\n#     form: Optional form schema for the approval page.\n#     self_approval: Whether the user who triggered the flow can approve it (default True).\n#     key: Optional checkpoint key naming this approval step.\n#     skin: ``\"minimal\"`` shows approvers only the request (form and approve/reject)\n#         instead of the detailed page with the workflow's details.\n#     description: Shown to approvers above the form: a string, or a rich value such as\n#         ``{\"markdown\": \"...\"}``.\n# \n# Example::\n# \n#     urls = await step(\"urls\", lambda: get_approval_urls(\"manager\"))\n#     await step(\"notify\", lambda: send_email(urls[\"resume\"], urls[\"cancel\"]))\n#     result = await wait_for_approval(key=\"manager\", timeout=3600)\nasync def wait_for_approval(timeout: int = 1800, form: dict | None = None, self_approval: bool = True, key: str | None = None, skin: Literal['detailed', 'minimal'] | None = None, description: str | dict | None = None) -> dict\n\n# Process items in parallel with optional concurrency control.\n# \n# Each item is processed by calling ``fn(item)``, which should be a @task.\n# Items are dispatched in batches of ``concurrency`` (default: all at once).\n# \n# Example::\n# \n#     @task\n#     async def process(item: str):\n#         ...\n# \n#     results = await parallel(items, process, concurrency=5)\nasync def parallel(items, fn, *, concurrency: Optional[int] = None)\n\n# Commit Kafka offsets for a trigger with auto_commit disabled.\n# \n# Args:\n#     trigger_path: Path to the Kafka trigger (from event['wm_trigger']['trigger_path'])\n#     topic: Kafka topic name (from event['topic'])\n#     partition: Partition number (from event['partition'])\n#     offset: Message offset to commit (from event['offset'])\ndef commit_kafka_offsets(trigger_path: str, topic: str, partition: int, offset: int) -> None\n\n",
		"datatable": "# Data tables: schema changes, migrations and permissions\n\nA data table is a PostgreSQL database managed by Windmill and shared by the whole workspace: scripts, flows and apps reach it by name, `main` being the default. It is configured in the workspace settings (Data Tables), never created by SQL.\n\n## Changing the schema\n\nA data table with migrations enabled keeps its schema as an ordered list of versioned SQL migrations, each an `up` and an optional `down`. That list is what git sync and a workspace merge carry, and what rebuilds the schema in another workspace. So first find out whether migrations are enabled on the data table:\n\n- **Enabled**: make every schema change (CREATE, ALTER, DROP, anything but row data) as a new migration. A change made with plain SQL exists in that one database only, and the history no longer describes it.\n- **Not enabled**: schema changes are plain SQL. Enabling migrations is a workspace admin's decision, made in the Data Tables settings, not a step to take on the way to something else.\n\nWriting a migration:\n\n- Wrap the statements in `BEGIN; ... END;` so it applies atomically. Leave the frame out only for a statement that cannot run in a transaction, such as `CREATE INDEX CONCURRENTLY`: such a migration can fail half applied.\n- Give it a `down` that undoes the `up` whenever the change is reversible. Without one it cannot be rolled back.\n- Declare the schemas it changes on a leading line of its `up`, `-- schemas sales, billing`, and qualify every name with its schema (`sales.orders`). A migration waits only on earlier migrations whose schemas overlap its own. One that declares none applies to the whole database and every later migration waits for it, so leave the line out only for a change that really spans the database, such as an extension.\n- Migrations apply oldest first. One that is applied is not edited: correct it with a new migration.\n- Row data is not schema. Insert, update and delete rows with plain SQL, migrations or not.\n\n## A migration is not a draft\n\nA migration is saved to the workspace and changes the database as soon as it is applied, for everything that reads those tables. To try a schema out without touching tables already in use:\n\n- put the new tables in a schema of their own, created by the same migration (`CREATE SCHEMA IF NOT EXISTS`), and point what you are building at it. A raw app names its schema in its `data` config, which is why a new app gets a new schema by default;\n- or work in a workspace fork whose data table was cloned when the fork was created (\"Clone schema only\" or \"Clone schema and data\"): migrations applied there change that copy alone, and reach the parent workspace when the fork is merged. A fork that kept the original data table points at the parent's database, so a migration applied in it changes the parent's tables at once.\n\nAltering or dropping a table other scripts or apps use is neither of those: say what depends on it before doing it.\n\n## Roles and permissions\n\nData table roles are a self-hosted Enterprise Edition feature, for data tables on managed Postgres (the instance database or the external managed instance). Without them, and always on a data table backed by a Postgres resource, everyone who can use the data table reaches all of it: there is no per-user, per-schema or per-row access to promise.\n\nA data table under roles is reached as a Postgres role, and Postgres enforces what that role was granted:\n\n- roles are assigned, per data table, to users, groups or folders. `admin` is the connection the data table had before roles: it owns the existing objects and ignores grants;\n- a connection either names a role (`wmill.datatable('main', { role: 'analyst' })`, `role='analyst'` in Python, `datatable://main?role=analyst`) and holds that role alone, or names none and is resolved from the data table's default role;\n- `permission denied` means the role in use lacks a grant. Say which role and which object: the grant is a data table admin's to give, in the roles drawer of the Database Manager. Do not switch to `admin` to get past it;\n- a migration runs as the role its SQL declares on its first line, `-- role <name>`, the same one in its `down`. One that declares none runs as `admin`, which only those who may use `admin` can do.\n\nRead the Permissions section of https://www.windmill.dev/docs/core_concepts/persistent_storage/data_tables before designing who reaches what: it covers tenants, grants, the default role and the roles a connection that names none ends up holding, rows that differ per viewer through row-level security, and how forks and raw apps behave under roles.\n\n\n# Datatable SQL SDK reference\n\nDatatables are workspace-scoped managed PostgreSQL databases. In chat, explore and shape them with the datatable tools you were given. The reference below is for code you author inside runnables (inline app runnables, scripts, or flow rawscript modules) that reads or writes datatable data at runtime.\n\n- A runnable accesses a datatable via `wmill.datatable()` (the default \"main\") or `wmill.datatable('<name>')`, referencing tables as `schema.table`.\n- Use parameterized queries (the tagged template in TypeScript, `$1`/`$2` placeholders in Python) — never interpolate untrusted values into SQL strings.\n\n## TypeScript Datatable API (windmill-client)\n\nImport: `import * as wmill from 'windmill-client'`\n\nSQL statement object with query content, arguments, and execution methods\n```typescript\ntype SqlStatement<T> = {\n  /** Raw SQL content with formatted arguments */\n  content: string;\n\n  /** Argument values keyed by parameter name */\n  args: Record<string, any>;\n\n  /**\n  * Execute the SQL query and return results\n  * @param params - Optional parameters including result collection mode\n  * @returns Query results based on the result collection mode\n  */\n  fetch<ResultCollectionT extends ResultCollection = \"last_statement_all_rows\">(\n  params?: FetchParams<ResultCollectionT | ResultCollection> // The union is for auto-completion\n  ): Promise<SqlResult<T, ResultCollectionT>>;\n\n  /**\n  * Execute the SQL query and return only the first row\n  * @param params - Optional parameters\n  * @returns First row of the query result\n  */\n  fetchOne(\n  params?: Omit<FetchParams<\"last_statement_first_row\">, \"resultCollection\">\n  ): Promise<SqlResult<T, \"last_statement_first_row\">>;\n\n  /**\n  * Execute the SQL query and return only the first row as a scalar value\n  * @param params - Optional parameters\n  * @returns First row of the query result\n  */\n  fetchOneScalar(\n  params?: Omit<\n  FetchParams<\"last_statement_first_row_scalar\">,\n  \"resultCollection\"\n  >\n  ): Promise<SqlResult<T, \"last_statement_first_row_scalar\">>;\n\n  /**\n  * Execute the SQL query without fetching rows\n  * @param params - Optional parameters\n  */\n  execute(\n  params?: Omit<FetchParams<\"last_statement_first_row\">, \"resultCollection\">\n  ): Promise<void>;\n};\n```\n\n```typescript\n// Template tag function: sql`SELECT * FROM table WHERE id = ${id}`.fetch()\ninterface DatatableSqlTemplateFunction {\n  // Tagged template usage:\n  <T = any>(strings: TemplateStringsArray, ...values: any[]): SqlStatement<T>;\n  query<T = any>(sql: string, ...params: any[]): SqlStatement<T>;\n};\n```\n\nCreate a SQL template function for PostgreSQL/datatable queries\n@param name - Database/datatable name (default: \"main\")\n@param opts.role - Connect as this data table role instead of the data table's default one.\n  Only meaningful on a data table under roles, and only for a role you are a tenant of.\n@returns SQL template function for building parameterized queries\n@example\nlet sql = wmill.datatable()\nlet name = 'Robin'\nlet age = 21\nawait sql`\n  SELECT * FROM friends\n    WHERE name = ${name} AND age = ${age}::int\n`.fetch()\n@example\n// Read through a restricted role\nlet sql = wmill.datatable(\"main\", { role: \"analytics\" })\n```typescript\nfunction datatable(name: string = \"main\",\n  opts?: DatatableOptions): DatatableSqlTemplateFunction\n```\n",
		"datatable.python3": "# Data tables: schema changes, migrations and permissions\n\nA data table is a PostgreSQL database managed by Windmill and shared by the whole workspace: scripts, flows and apps reach it by name, `main` being the default. It is configured in the workspace settings (Data Tables), never created by SQL.\n\n## Changing the schema\n\nA data table with migrations enabled keeps its schema as an ordered list of versioned SQL migrations, each an `up` and an optional `down`. That list is what git sync and a workspace merge carry, and what rebuilds the schema in another workspace. So first find out whether migrations are enabled on the data table:\n\n- **Enabled**: make every schema change (CREATE, ALTER, DROP, anything but row data) as a new migration. A change made with plain SQL exists in that one database only, and the history no longer describes it.\n- **Not enabled**: schema changes are plain SQL. Enabling migrations is a workspace admin's decision, made in the Data Tables settings, not a step to take on the way to something else.\n\nWriting a migration:\n\n- Wrap the statements in `BEGIN; ... END;` so it applies atomically. Leave the frame out only for a statement that cannot run in a transaction, such as `CREATE INDEX CONCURRENTLY`: such a migration can fail half applied.\n- Give it a `down` that undoes the `up` whenever the change is reversible. Without one it cannot be rolled back.\n- Declare the schemas it changes on a leading line of its `up`, `-- schemas sales, billing`, and qualify every name with its schema (`sales.orders`). A migration waits only on earlier migrations whose schemas overlap its own. One that declares none applies to the whole database and every later migration waits for it, so leave the line out only for a change that really spans the database, such as an extension.\n- Migrations apply oldest first. One that is applied is not edited: correct it with a new migration.\n- Row data is not schema. Insert, update and delete rows with plain SQL, migrations or not.\n\n## A migration is not a draft\n\nA migration is saved to the workspace and changes the database as soon as it is applied, for everything that reads those tables. To try a schema out without touching tables already in use:\n\n- put the new tables in a schema of their own, created by the same migration (`CREATE SCHEMA IF NOT EXISTS`), and point what you are building at it. A raw app names its schema in its `data` config, which is why a new app gets a new schema by default;\n- or work in a workspace fork whose data table was cloned when the fork was created (\"Clone schema only\" or \"Clone schema and data\"): migrations applied there change that copy alone, and reach the parent workspace when the fork is merged. A fork that kept the original data table points at the parent's database, so a migration applied in it changes the parent's tables at once.\n\nAltering or dropping a table other scripts or apps use is neither of those: say what depends on it before doing it.\n\n## Roles and permissions\n\nData table roles are a self-hosted Enterprise Edition feature, for data tables on managed Postgres (the instance database or the external managed instance). Without them, and always on a data table backed by a Postgres resource, everyone who can use the data table reaches all of it: there is no per-user, per-schema or per-row access to promise.\n\nA data table under roles is reached as a Postgres role, and Postgres enforces what that role was granted:\n\n- roles are assigned, per data table, to users, groups or folders. `admin` is the connection the data table had before roles: it owns the existing objects and ignores grants;\n- a connection either names a role (`wmill.datatable('main', { role: 'analyst' })`, `role='analyst'` in Python, `datatable://main?role=analyst`) and holds that role alone, or names none and is resolved from the data table's default role;\n- `permission denied` means the role in use lacks a grant. Say which role and which object: the grant is a data table admin's to give, in the roles drawer of the Database Manager. Do not switch to `admin` to get past it;\n- a migration runs as the role its SQL declares on its first line, `-- role <name>`, the same one in its `down`. One that declares none runs as `admin`, which only those who may use `admin` can do.\n\nRead the Permissions section of https://www.windmill.dev/docs/core_concepts/persistent_storage/data_tables before designing who reaches what: it covers tenants, grants, the default role and the roles a connection that names none ends up holding, rows that differ per viewer through row-level security, and how forks and raw apps behave under roles.\n\n\n# Datatable SQL SDK reference\n\nDatatables are workspace-scoped managed PostgreSQL databases. In chat, explore and shape them with the datatable tools you were given. The reference below is for code you author inside runnables (inline app runnables, scripts, or flow rawscript modules) that reads or writes datatable data at runtime.\n\n- A runnable accesses a datatable via `wmill.datatable()` (the default \"main\") or `wmill.datatable('<name>')`, referencing tables as `schema.table`.\n- Use parameterized queries (the tagged template in TypeScript, `$1`/`$2` placeholders in Python) — never interpolate untrusted values into SQL strings.\n\n## Python Datatable API (wmill)\n\nImport: `import wmill`\n\n# Get a DataTable client for SQL queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n#     role: Connect as this data table role instead of the data table's default one.\n# \n# Returns:\n#     DataTableClient instance\ndef datatable(name: str = 'main', *, role: Optional[str] = None) -> DataTableClient\n\n# Client for executing SQL queries against Windmill DataTables.\nclass DataTableClient:\n    # Initialize DataTableClient.\n    # \n    # Args:\n    #     client: Windmill client instance\n    #     name: DataTable name\n    #     role: Data table role to connect as, or None for the data table's default\n    def __init__(client: Windmill, name: str, role: Optional[str] = None)\n\n    # Execute a SQL query against the DataTable.\n    # \n    # Args:\n    #     sql: SQL query string with $1, $2, etc. placeholders\n    #     *args: Positional arguments to bind to query placeholders\n    # \n    # Returns:\n    #     SqlQuery instance for fetching results\n    def query(sql: str, *args) -> SqlQuery\n\n\n# Query result handler for DataTable and DuckLake queries.\nclass SqlQuery:\n    # Initialize SqlQuery.\n    # \n    # Args:\n    #     sql: SQL query string\n    #     fetch_fn: Function to execute the query\n    def __init__(sql: str, fetch_fn)\n\n    # Execute query and fetch results.\n    # \n    # Args:\n    #     result_collection: Optional result collection mode\n    # \n    # Returns:\n    #     Query results\n    def fetch(result_collection: str | None = None)\n\n    # Execute query and fetch first row of results.\n    # \n    # Returns:\n    #     First row of query results\n    def fetch_one()\n\n    # Execute query and fetch first row of results. Return result as a scalar value.\n    # \n    # Returns:\n    #     First row of query result as a scalar value\n    def fetch_one_scalar()\n\n    # Execute query and don't return any results.\n    #         \n    def execute()\n\n\n",
		"datatable.go": "# Data tables: schema changes, migrations and permissions\n\nA data table is a PostgreSQL database managed by Windmill and shared by the whole workspace: scripts, flows and apps reach it by name, `main` being the default. It is configured in the workspace settings (Data Tables), never created by SQL.\n\n## Changing the schema\n\nA data table with migrations enabled keeps its schema as an ordered list of versioned SQL migrations, each an `up` and an optional `down`. That list is what git sync and a workspace merge carry, and what rebuilds the schema in another workspace. So first find out whether migrations are enabled on the data table:\n\n- **Enabled**: make every schema change (CREATE, ALTER, DROP, anything but row data) as a new migration. A change made with plain SQL exists in that one database only, and the history no longer describes it.\n- **Not enabled**: schema changes are plain SQL. Enabling migrations is a workspace admin's decision, made in the Data Tables settings, not a step to take on the way to something else.\n\nWriting a migration:\n\n- Wrap the statements in `BEGIN; ... END;` so it applies atomically. Leave the frame out only for a statement that cannot run in a transaction, such as `CREATE INDEX CONCURRENTLY`: such a migration can fail half applied.\n- Give it a `down` that undoes the `up` whenever the change is reversible. Without one it cannot be rolled back.\n- Declare the schemas it changes on a leading line of its `up`, `-- schemas sales, billing`, and qualify every name with its schema (`sales.orders`). A migration waits only on earlier migrations whose schemas overlap its own. One that declares none applies to the whole database and every later migration waits for it, so leave the line out only for a change that really spans the database, such as an extension.\n- Migrations apply oldest first. One that is applied is not edited: correct it with a new migration.\n- Row data is not schema. Insert, update and delete rows with plain SQL, migrations or not.\n\n## A migration is not a draft\n\nA migration is saved to the workspace and changes the database as soon as it is applied, for everything that reads those tables. To try a schema out without touching tables already in use:\n\n- put the new tables in a schema of their own, created by the same migration (`CREATE SCHEMA IF NOT EXISTS`), and point what you are building at it. A raw app names its schema in its `data` config, which is why a new app gets a new schema by default;\n- or work in a workspace fork whose data table was cloned when the fork was created (\"Clone schema only\" or \"Clone schema and data\"): migrations applied there change that copy alone, and reach the parent workspace when the fork is merged. A fork that kept the original data table points at the parent's database, so a migration applied in it changes the parent's tables at once.\n\nAltering or dropping a table other scripts or apps use is neither of those: say what depends on it before doing it.\n\n## Roles and permissions\n\nData table roles are a self-hosted Enterprise Edition feature, for data tables on managed Postgres (the instance database or the external managed instance). Without them, and always on a data table backed by a Postgres resource, everyone who can use the data table reaches all of it: there is no per-user, per-schema or per-row access to promise.\n\nA data table under roles is reached as a Postgres role, and Postgres enforces what that role was granted:\n\n- roles are assigned, per data table, to users, groups or folders. `admin` is the connection the data table had before roles: it owns the existing objects and ignores grants;\n- a connection either names a role (`wmill.datatable('main', { role: 'analyst' })`, `role='analyst'` in Python, `datatable://main?role=analyst`) and holds that role alone, or names none and is resolved from the data table's default role;\n- `permission denied` means the role in use lacks a grant. Say which role and which object: the grant is a data table admin's to give, in the roles drawer of the Database Manager. Do not switch to `admin` to get past it;\n- a migration runs as the role its SQL declares on its first line, `-- role <name>`, the same one in its `down`. One that declares none runs as `admin`, which only those who may use `admin` can do.\n\nRead the Permissions section of https://www.windmill.dev/docs/core_concepts/persistent_storage/data_tables before designing who reaches what: it covers tenants, grants, the default role and the roles a connection that names none ends up holding, rows that differ per viewer through row-level security, and how forks and raw apps behave under roles.\n\n\n# Datatable SQL SDK reference\n\nDatatables are workspace-scoped managed PostgreSQL databases. In chat, explore and shape them with the datatable tools you were given. The reference below is for code you author inside runnables (inline app runnables, scripts, or flow rawscript modules) that reads or writes datatable data at runtime.\n\n- A runnable accesses a datatable via `wmill.datatable()` (the default \"main\") or `wmill.datatable('<name>')`, referencing tables as `schema.table`.\n- Use parameterized queries (the tagged template in TypeScript, `$1`/`$2` placeholders in Python) — never interpolate untrusted values into SQL strings.\n\n## TypeScript Datatable API (windmill-client)\n\nImport: `import * as wmill from 'windmill-client'`\n\nSQL statement object with query content, arguments, and execution methods\n```typescript\ntype SqlStatement<T> = {\n  /** Raw SQL content with formatted arguments */\n  content: string;\n\n  /** Argument values keyed by parameter name */\n  args: Record<string, any>;\n\n  /**\n  * Execute the SQL query and return results\n  * @param params - Optional parameters including result collection mode\n  * @returns Query results based on the result collection mode\n  */\n  fetch<ResultCollectionT extends ResultCollection = \"last_statement_all_rows\">(\n  params?: FetchParams<ResultCollectionT | ResultCollection> // The union is for auto-completion\n  ): Promise<SqlResult<T, ResultCollectionT>>;\n\n  /**\n  * Execute the SQL query and return only the first row\n  * @param params - Optional parameters\n  * @returns First row of the query result\n  */\n  fetchOne(\n  params?: Omit<FetchParams<\"last_statement_first_row\">, \"resultCollection\">\n  ): Promise<SqlResult<T, \"last_statement_first_row\">>;\n\n  /**\n  * Execute the SQL query and return only the first row as a scalar value\n  * @param params - Optional parameters\n  * @returns First row of the query result\n  */\n  fetchOneScalar(\n  params?: Omit<\n  FetchParams<\"last_statement_first_row_scalar\">,\n  \"resultCollection\"\n  >\n  ): Promise<SqlResult<T, \"last_statement_first_row_scalar\">>;\n\n  /**\n  * Execute the SQL query without fetching rows\n  * @param params - Optional parameters\n  */\n  execute(\n  params?: Omit<FetchParams<\"last_statement_first_row\">, \"resultCollection\">\n  ): Promise<void>;\n};\n```\n\n```typescript\n// Template tag function: sql`SELECT * FROM table WHERE id = ${id}`.fetch()\ninterface DatatableSqlTemplateFunction {\n  // Tagged template usage:\n  <T = any>(strings: TemplateStringsArray, ...values: any[]): SqlStatement<T>;\n  query<T = any>(sql: string, ...params: any[]): SqlStatement<T>;\n};\n```\n\nCreate a SQL template function for PostgreSQL/datatable queries\n@param name - Database/datatable name (default: \"main\")\n@param opts.role - Connect as this data table role instead of the data table's default one.\n  Only meaningful on a data table under roles, and only for a role you are a tenant of.\n@returns SQL template function for building parameterized queries\n@example\nlet sql = wmill.datatable()\nlet name = 'Robin'\nlet age = 21\nawait sql`\n  SELECT * FROM friends\n    WHERE name = ${name} AND age = ${age}::int\n`.fetch()\n@example\n// Read through a restricted role\nlet sql = wmill.datatable(\"main\", { role: \"analytics\" })\n```typescript\nfunction datatable(name: string = \"main\",\n  opts?: DatatableOptions): DatatableSqlTemplateFunction\n```\n\n\n## Python Datatable API (wmill)\n\nImport: `import wmill`\n\n# Get a DataTable client for SQL queries.\n# \n# Args:\n#     name: Database name (default: \"main\")\n#     role: Connect as this data table role instead of the data table's default one.\n# \n# Returns:\n#     DataTableClient instance\ndef datatable(name: str = 'main', *, role: Optional[str] = None) -> DataTableClient\n\n# Client for executing SQL queries against Windmill DataTables.\nclass DataTableClient:\n    # Initialize DataTableClient.\n    # \n    # Args:\n    #     client: Windmill client instance\n    #     name: DataTable name\n    #     role: Data table role to connect as, or None for the data table's default\n    def __init__(client: Windmill, name: str, role: Optional[str] = None)\n\n    # Execute a SQL query against the DataTable.\n    # \n    # Args:\n    #     sql: SQL query string with $1, $2, etc. placeholders\n    #     *args: Positional arguments to bind to query placeholders\n    # \n    # Returns:\n    #     SqlQuery instance for fetching results\n    def query(sql: str, *args) -> SqlQuery\n\n\n# Query result handler for DataTable and DuckLake queries.\nclass SqlQuery:\n    # Initialize SqlQuery.\n    # \n    # Args:\n    #     sql: SQL query string\n    #     fetch_fn: Function to execute the query\n    def __init__(sql: str, fetch_fn)\n\n    # Execute query and fetch results.\n    # \n    # Args:\n    #     result_collection: Optional result collection mode\n    # \n    # Returns:\n    #     Query results\n    def fetch(result_collection: str | None = None)\n\n    # Execute query and fetch first row of results.\n    # \n    # Returns:\n    #     First row of query results\n    def fetch_one()\n\n    # Execute query and fetch first row of results. Return result as a scalar value.\n    # \n    # Returns:\n    #     First row of query result as a scalar value\n    def fetch_one_scalar()\n\n    # Execute query and don't return any results.\n    #         \n    def execute()\n\n\n",
		"pipeline": "# Data pipeline authoring\n\nA **data pipeline** is NOT a flow. A flow is one runnable that orchestrates steps internally. A data pipeline is a set of **independent scripts**, each deployed on its own, that form a DAG by reading and writing shared **storage assets** (DuckLake tables, data tables, S3 objects, volumes, resources) and by declaring execution **triggers**. The pipeline is visualized and edited at `/pipeline/<folder>`; every node is a normal workspace script that happens to carry pipeline annotations. When the user asks for a \"data pipeline\" (or to \"ingest / transform / materialize\" data across steps), build pipeline-annotated scripts — do NOT build a flow.\n\n## Default to DuckDB + DuckLake\n\nA pipeline node that produces a table should almost always be a **`duckdb`** node that materializes its output into a **DuckLake** table with `-- materialize ducklake://<name>/<table>` (in a DuckDB node the annotation uses SQL `--` comment syntax; write the body as a bare `SELECT` and let the runtime do the write). DuckLake is the default lakehouse store for pipelines and is the shape the pipeline editor is built around, so prefer it unless the work specifically calls for something else:\n\n- `postgresql` / data tables — only for row-level, OLTP-style mutations against an existing Postgres data table (frequent single-row upserts/updates, transactional reads that an app queries live).\n- `bun` / `python3` — only for work that doesn't map to SQL: calling an external API, wrangling files, arbitrary glue. When such a node produces tabular data for downstream steps, write it **straight into DuckLake** (or a data table) from the same script — see \"Ingesting from Python / TypeScript\" below — rather than inventing a parallel store.\n\nDo not spread a pipeline across postgres, S3, and DuckLake when one DuckLake lake would do; a consistent DuckLake lakehouse is the goal.\n\n## Ingesting from Python / TypeScript\n\nA node that fetches data in code (an API, a SaaS export, a scraper) writes the rows **directly** into their DuckLake table with `wmill.ducklake()`, or into a data table with `wmill.datatable()`. DuckLake already stores every table as parquet files in the workspace object storage, so **never stage the raw payload first** — no intermediate JSON / CSV / parquet file in S3 and no second \"load the raw file\" node. One ingestion node, one table.\n\nPass the rows as a query argument (a list of dicts / array of objects is sent as JSON) and let SQL unnest them; declare the column types so the table gets a real schema:\n\n```python\n# pipeline\n# on schedule\nimport wmill\n\ndef main():\n    rows = fetch_charges()  # list[dict]\n    lake = wmill.ducklake()\n    lake.query(\"\"\"\n        CREATE OR REPLACE TABLE stripe_charges AS\n        SELECT unnest(from_json($rows, '[{\"id\":\"VARCHAR\",\"amount\":\"BIGINT\",\"created\":\"BIGINT\"}]'), recursive := true)\n    \"\"\", rows=rows).fetch()\n```\n\n```ts\n// pipeline\n// on schedule\nimport * as wmill from \"windmill-client\"\n\nexport async function main() {\n  const rows = await fetchCharges()\n  const lake = wmill.ducklake()\n  await lake`CREATE OR REPLACE TABLE stripe_charges AS\n    SELECT unnest(from_json(${rows}, '[{\"id\":\"VARCHAR\",\"amount\":\"BIGINT\",\"created\":\"BIGINT\"}]'), recursive := true)`.fetch()\n}\n```\n\n- Use `INSERT INTO <table> SELECT ...` instead of `CREATE OR REPLACE` to append (an incremental pull), with a one-time `CREATE TABLE IF NOT EXISTS` first.\n- Assign the client to a variable (`lake = wmill.ducklake()`) and keep the SQL a **string literal** (Python) or a template without `sql.raw` (TS): that is what lets the graph record the node's write to `ducklake://main/<table>`. A chained `wmill.ducklake().query(...)` or SQL built in a variable records no output, so the downstream edge never forms.\n- `wmill.ducklake(\"<name>\")` targets a non-default catalog; `wmill.datatable(\"<name>\")` writes a Postgres data table the same way (Python `db.query(sql, *args)` with `$1` placeholders, TS the same template form) — use it only when the data belongs in Postgres (see above).\n- The downstream node triggers on the table itself: `-- on ducklake://main/stripe_charges`.\n\n## Storage prerequisites\n\nA DuckLake pipeline only runs once the workspace has **object storage** (S3 / Azure Blob / GCS) **and a DuckLake catalog** configured — DuckLake tables and `s3://` assets can't be materialized or read without it. Check which DuckLake catalogs the workspace has before you build.\nDrafting the annotated scripts does not require storage, but the pipeline can't ingest, materialize, or read its assets until it exists. So if there is none (or the user hits \"storage not configured\" errors), say so and give the right next step **by role**:\n\n- a workspace **admin** sets it up in Workspace settings → Object Storage (add an S3/Azure/GCS storage), then adds a DuckLake catalog on top of it;\n- anyone **without admin rights** should ask a workspace admin to configure object storage + a DuckLake catalog.\n\nNever hand back a DuckLake pipeline that cannot run without flagging the missing storage and pointing to who sets it up.\n\n## What makes a script a pipeline node\n\nA script joins the pipeline when its source begins with the `pipeline` annotation as a top-of-file comment, **written in the script's own comment syntax** — `//` for TS/JS (bun), `--` for SQL (DuckDB/Postgres), `#` for Python/Bash. So it's `-- pipeline` in a DuckDB node, `# pipeline` in a Python node, `// pipeline` in a bun node. Every annotation below uses that same prefix (the `//` shown is the TS form). All other wiring is expressed as annotation comments near the top of the file:\n\n- `// on <ref>` — declares an execution-DAG **input** (what triggers/feeds this node). `<ref>` is either:\n  - an **asset URI** (the node runs when that asset is produced upstream): `ducklake://main/orders`, `datatable://main/users`, `$res:f/folder/my_resource`, `volume://name/path`, or an S3 object (see the S3 storage-form rule below).\n  - a **native trigger kind**: `schedule`, `webhook`, `email`, `kafka`, `mqtt`, `amqp`, `nats`, `postgres`, `sqs`, `gcp`, or `data_upload` (a user-uploaded S3 file). For these the actual trigger row (cron, topic, …) is created separately; the annotation only declares the binding. **`data_upload` is special**: there is no trigger row — the node instead declares an **`S3Object` input parameter** fed by the auto-generated upload picker; it never hard-codes a key. Any language can be the `data_upload` node:\n    - Python (has `import wmill`): `def main(file: wmill.S3Object):` then `wmill.load_s3_file(file)`; TS (has `import * as wmill from \"windmill-client\"`): `export async function main(file: wmill.S3Object)`. Qualify the type as `wmill.S3Object` (or add `from wmill import S3Object` / `import { S3Object } from \"windmill-client\"`) — a bare `S3Object` is undefined.\n    - **DuckDB** takes the s3object arg via a `-- $<name> (s3object)` declaration and reads it directly, so a single DuckDB node can ingest **and** materialize:\n      ```\n      -- pipeline\n      -- on data_upload\n      -- materialize ducklake://main/raw_uploads\n      -- $file (s3object)\n      SELECT * FROM read_csv($file)\n      ```\n- **Outputs** are inferred from what the body writes — a `CREATE TABLE`, a `wmill.writeS3File(...)`, a DuckLake/datatable write. To declare a managed output explicitly, use `// materialize <asset-uri>`.\n- Optional badges: `// partitioned <daily|hourly|weekly|monthly|dynamic>`, `// freshness <duration>` (e.g. `1h`), `// tag <worker-tag>`, `// retry <count> [delay]`, `// data_test <kind> ...` (managed DuckLake targets only — deploy rejects it beside a `dbt://` one), `// measure <name> = <agg> [where <pred>]` and `// dimension <name> = <expr>` (see \"Declared metrics\" below).\n\n## S3 object wiring (storage form matters)\n\nAn `s3://` URI's first slashes select the **storage**, not part of the key — get this wrong and the producer/consumer edge silently won't connect:\n\n- `s3:///<key>` (**triple** slash, empty first segment) = the **default** workspace storage. A downstream node reading or triggering on that object uses `s3:///<key>` — e.g. DuckDB `-- on s3:///orders/2024.parquet` and `read_parquet('s3:///orders/2024.parquet')`.\n- `s3://<storage>/<key>` (**double** slash, non-empty first segment) = a **named secondary** storage called `<storage>` — so `s3://ingest/x` means storage `ingest`, key `x`, NOT key `ingest/x`. Only use this when the object genuinely lives in a configured secondary storage; never invent a bucket/storage name for a default-storage object (it breaks the edge).\n\nTo make the producer side visible to lineage, a Python/TS node MUST pass the **`S3Object` form**, not a bare key string: Python `wmill.write_s3_file(wmill.S3Object(s3=\"<key>\"), data)` (or the import-free dict `{\"s3\": \"<key>\"}`), TS `wmill.writeS3File({ s3: \"<key>\" }, data)`. That records the default-storage asset `/<key>`, which a downstream `s3:///<key>` reader connects to (same key both sides). A bare `write_s3_file(\"<key>\", ...)` records **no** asset and produces **no** edge. Add `storage=\"<name>\"` only for a named secondary storage.\n\nThe key must be a **string literal** — the graph parser is static and cannot follow a variable, f-string, or computed path, so `write_s3_file(wmill.S3Object(s3=key_var), ...)` records no edge. Inline the literal (`s3=\"events/user_events.parquet\"`) on both the writing and reading node. The same rule applies to every asset URI in an annotation or SDK call (`ducklake://`, `datatable://`, `s3://`): write them literally, not via a variable.\n\n## Materialize (the managed output)\n\n> **A MANAGED `// materialize` is DuckDB-only**, and its target must be a DuckLake table (`ducklake://<name>/<table>`). Deploy **rejects** a `ducklake://` `// materialize` on any other language (`python3`, `bun`, `postgresql`). For a non-DuckDB node writing the lake, do **not** use `// materialize` — write the table itself with `wmill.ducklake()` (see \"Ingesting from Python / TypeScript\") and let the output be inferred. Use `duckdb` when a node should materialize a DuckLake table.\n>\n> The one target ANY language may declare (except a dbt script, whose writes come from its manifest) is a **warehouse relation**: `// materialize manual dbt://<warehouse>/<schema>/<name>`, where `<warehouse>` is a warehouse the workspace configures under Settings → dbt. `manual` is the only mode it has — nothing generates warehouse DDL, so the node issues its own write (a postgresql `CREATE TABLE` / `INSERT`, an SDK load, …) and the annotation records the outcome. Use it on an ingestion node whose output a dbt project reads as a `source`: the declared relation and the dbt model land on ONE graph node, and a downstream `// on dbt://<warehouse>/<schema>/<name>` fires when the ingestion node completes.\n\nA managed `// materialize ducklake://<name>/<table>` tells the runtime to write the node's output table **for you**: write the body as a single `SELECT` and the runtime wraps it in the create/replace — do **not** also write your own `CREATE TABLE` / `INSERT`. (The opposite holds for the `dbt://` target above: there the node writes its own DDL and the strategies below do not apply.) Write strategy:\n\n- no option → **replace** the whole table each run (full refresh; the only mode whose output columns may change);\n- `// materialize <uri> append` → INSERT-append rows (incremental);\n- `// materialize <uri> key=<col>` → merge/upsert on `<col>`.\n\n`// materialize manual <uri>` opts **out** of managed writes — the script writes its own DDL and the annotation only records the output asset for lineage.\n\n`materialize` pairs with partitioning for incremental pipelines: a `// partitioned <daily|hourly|weekly|monthly|dynamic>` node runs **once per partition** (append/merge into a fixed-schema table). The `{partition}` token, usable in any asset URI **and** in the body SQL, is replaced at run time by the current partition's **identity string**:\n\n- To filter the source to the active slice on a time grain, use the runtime-injected macro: `WHERE wm_partition(<ts_col>) = {partition}`. `wm_partition(ts)` buckets a timestamp in exactly the identity format the runtime uses for daily/hourly/weekly/monthly, so it always matches; never hand-write a `strftime` format.\n- Do NOT write `= TIMESTAMP {partition}`: the identity string is not a valid timestamp literal for hourly/weekly/monthly and errors at run time.\n- For `dynamic` partitioning the identity is the caller-supplied key (not a timestamp, no macro), so filter on it directly: `WHERE <your_key_col> = {partition}`.\n\n`materialize` is an output **declaration** on a node — not a command. There is no \"materialize run\".\n\n## Declared metrics (`measure` / `dimension`)\n\nOn a node that materializes a DuckLake table, `// measure <name> = <aggregate> [where <predicate>]` names the canonical way to aggregate that table (e.g. `// measure revenue = sum(amount) where not is_refund`), and `// dimension <name> = <expr>` names a way to slice it (e.g. `// dimension region = region`, `// dimension month = date_trunc('month', ordered_at)`). They execute nothing: they are catalogued at deploy so the editor and other agents reuse the definition instead of re-deriving it and silently disagreeing.\n\n- Keep the predicate in the `where` clause rather than folding it into the aggregate: it is rendered as `<agg> FILTER (WHERE <pred>)`, which is what lets two measures with different predicates share one GROUP BY.\n- DuckLake-only, and only meaningful next to `// materialize`.\n- Declare one when a number carries a judgement call someone else would get wrong (refunds excluded, test rows dropped, which column is the amount); do NOT blanket every table with measures — an obvious `count(*)` earns nothing.\n- To use a metric another node declares, read that node and reuse its exact expression rather than guessing it.\n\n## How to build one\n\n1. Put every node in the **same folder**: `f/<folder>/<name>`. The folder is the pipeline.\n2. Write each node as its own script. Default to `duckdb` materializing into DuckLake (see \"Default to DuckDB + DuckLake\" above); pick `postgresql`, `bun`, or `python3` only when that section says the work calls for it.\n3. Start each body with `// pipeline`, then the `// on` input declarations, then the transform that writes the output.\n4. **Chain nodes by asset URI**: read an upstream node's output asset, then `// on <that-same-uri>` in the downstream node so the edge forms. Reuse exact asset paths from existing nodes rather than inventing parallel ones.\n5. Don't deploy nodes unless the user asks to. A pipeline only \"runs\" once its scripts are deployed and their triggers exist.\n\n## Example (DuckDB → DuckLake, scheduled ingest + downstream transform)\n\nNode `f/sales/orders_ingest` (runs on a schedule, materializes a DuckLake table):\n\n```sql\n-- pipeline\n-- on schedule\n-- materialize ducklake://main/orders\nSELECT * FROM read_csv('s3:///raw/orders/*.csv')\n```\n\nNode `f/sales/orders_daily` (runs when `orders` is produced, writes a rollup):\n\n```sql\n-- pipeline\n-- on ducklake://main/orders\n-- materialize ducklake://main/orders_daily\nSELECT date_trunc('day', ts) AS day, count(*) AS n\nFROM ducklake.main.orders GROUP BY 1\n```\n",
		"workspace_settings": "# Workspace settings\n\nEverything an admin sets on the Workspace settings page is one document: a mapping of top-level\nkeys, the same layout as the `settings.yaml` file `wmill sync` keeps. Only a workspace admin can\nread or change it.\n\n## Applying a document\n\n- A partial document changes only the keys it states; every key it omits is left as it is.\n- Each key is replaced whole, so read its current value and send it back edited: a nested object\n  given with one field replaces the stored object with that one field.\n- A key set to `null` clears that setting; an on/off switch cleared this way reads as `false`.\n- Each setting is written through the same path the settings page uses, with the same validation\n  and audit log. The writes are not one transaction: when one fails the apply stops, and the\n  error says what was already written.\n- A dry run reports the changes, as `before` / `after` per key, without writing anything.\n- `name` is never applied: rename a workspace on the settings page. `color` is applied only when\n  set to a value, so `null` does not clear it.\n- A key the server does not know is ignored and named in the warnings: check them, since a typo\n  otherwise reads as \"nothing to change\".\n- A setting this edition does not offer (Enterprise only) is refused, by the dry run too.\n\n## Secrets\n\n`slack_oauth_client_secret` is the one secret. A read shows `__SENSITIVE_AND_UNCHANGED__` in place\nof a set secret; sending that placeholder back keeps the stored value. A value starting with\n`$encrypted:` is decrypted with the workspace key, and one encrypted with another workspace's key\nis left unchanged with a warning; any other value is taken as plaintext. Never\nwrite a secret you were not given: keep the placeholder.\n\n## Keys\n\n| Key | Value |\n|---|---|\n| `auto_invite` | `{ enabled, mode: invite \\| add, operator, instance_groups?, instance_groups_roles? }`. The instance groups are managed only when `instance_groups` is stated; clearing a group removes the members it added. |\n| `error_handler` / `success_handler` | `{ path, extra_args?, muted_on_cancel?, muted_on_user_path? }`, `path` being `script/<path>` or `flow/<path>`. |\n| `error_handler_fallback_to_instance_alerts` | Report failures to the instance critical alert channels when no error handler is set. |\n| `webhook` | URL POSTed a JSON event on every change to the workspace's items. |\n| `ai_config` | Providers, default and completion models, custom prompts, `copilot_disabled`. |\n| `large_file_storage` | Workspace object storage: `{ type, s3_resource_path \\| azure_blob_resource_path \\| gcs_resource_path, public_resource?, secondary_storage?, advanced_permissions? }`. |\n| `git_sync` | `{ repositories: [...] }`, each with `git_repo_resource_path`, `script_path` and `settings` (include/exclude paths and types). Server-owned fields (webhook secrets, sync state, credentials) are never read nor written. |\n| `datatable` | `{ datatables: { <name>: { database: { resource_type, resource_path } } } }`, both required: `resource_type: postgresql` with `resource_path` a Postgres resource's path, or `instance`, or `external_instance` (Enterprise only), with `resource_path` the name of a database a superadmin set up. A fork's entry can instead be `{ reference: { workspace_id, datatable } }`, pointing at its parent's data table; keep it as is. `main` is the one scripts use by default. Role permissions are `datatable_permissions`. |\n| `datatable_permissions` | `{ <data table>: { default_role, roles: { <role>: [tenants] } } }`: who may connect to each data table as which role. Tenants are `u/<user>`, `g/<group>`, `f/<folder>` or `*`; `admin` is always a role, and `default_role: null` means no default. A data table the value omits is no longer under roles. Only a data table on a cluster Windmill manages can be. |\n| `protection_rules` | `{ <rule name>: { rules: [...], bypass_groups: [...], bypass_users: [...] } }`, rules among `DisableDirectDeployment`, `DisableWorkspaceForking`, `RestrictDeployToDeployers`, `RestrictAnonymousAppDeployment`, `RestrictPublicRunSharing`, `RestrictGuestAppDeployment`, `RequireIsolatedForks`. A rule the value omits is deleted. The dev workspace lock is not listed and cannot be set here. |\n| `ducklake` | `{ ducklakes: { <name>: { catalog, storage, ... } } }`. |\n| `dbt_warehouses` | dbt warehouse connections. |\n| `default_app` | Path of the app shown as the workspace home. |\n| `default_scripts` | `{ order?, hidden?, default_script_content? }` for the script language picker. |\n| `deploy_ui` | `{ include_path?, include_type? }`: what the deploy-to-workspace UI offers. |\n| `operator_settings` | What operators see and do: page visibility flags (`runs`, `schedules`, `resources`, `variables`, `assets`, `audit_logs`, `groups`, `folders`, `workers`, `triggers`) and write rights (`manage_schedules`, `manage_triggers`, `builder_flows`, `builder_apps`). A write right the value omits keeps its stored value. |\n| `mute_critical_alerts` | Mute the workspace's critical alerts. |\n| `slack_team_id`, `slack_name` | Read only: set by connecting Slack on the settings page. |\n| `slack_command_script` / `teams_command_script` | Script or flow handling the Slack / Teams command. |\n| `slack_oauth_client_id` / `slack_oauth_client_secret` | The workspace's own Slack OAuth app. Clearing either removes both. |\n| `public_app_execution_limit_per_minute` | Rate limit for public apps, `null` for none. |\n| `guest_access_enabled`, `guest_jwt_public_key`, `guest_jwt_jwks_url` | Guest access to public apps and the key that signs guest JWTs. |\n| `add_admins_and_developers_to_forks` | Add the workspace's admins and developers to each fork. |\n| `color` | Workspace color, `#rrggbb`. |\n"
	}
}