Diego ImbertandClaude Opus 5
a74f73ccb1
Putting it inside `custom_instance_pg_databases` was the wrong call, and it cost two ways. The catalog serializes a generated Postgres password per role, and that row is the operator-facing instance config, so the passwords reached `get_instance_config` and its YAML editor — a live cluster credential in a response body, a UI field and any log of either. Worse in the other direction: `to_settings_map` strips the catalog, so a full-row upsert of that key writes the row back without it and the catalog is gone, while the cluster keeps every login it described. `custom_instance_replication_pwd` is the precedent and says exactly why — a generated secret, written only by the server, never operator-authored, hidden so the config machinery cannot read, rewrite or drop it. The catalog is the same thing, so it now has the same shape: `datatable_roles`, in `HIDDEN_SETTINGS`, `PROTECTED_SETTINGS` and the agent-worker denylist. No redaction to keep in step with three code paths, and no way for a neighbouring write to take it out. Two races on the same shared documents. `edit_datatable_config` read the stored data tables outside its transaction and then wrote the whole `datatable` document, so a permissions save committing in between was silently rolled back; it now reads under `FOR UPDATE`. And `set_datatable_permissions` validated role ids against the catalog before opening its transaction, so a deletion in between let it write a deleted role back — including as the default, which every later job then fails on; it now holds the catalog lock and the settings row across validation and write. Completes the authorization contracts the previous commit claimed but did not finish: `read_datatable_entry` (which it named and missed), `resolve_governing_datatable`, whose whole job is to answer for a workspace the caller may not belong to, and `converge_connect_grants_with`, which had not inherited its wrapper's. Also the generic Python SDK reference: `_format_py_params` learned the bare `*` last time, but `extract_py_functions` is a second formatter and still rendered `datatable(name, role)`, so code written from that page passed a keyword-only argument positionally. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ti5HyeTikPMYyW8YSdiHR
Windmill CLI
A simple CLI allowing interactions with windmill from the command line.
You can find more information in Windmill Docs
Installation
Install the wmill CLI tool using npm install -g windmill-cli.
Update to the latest version using wmill upgrade.
Workspaces
To get started run wmill workspace add or use the instructions from the
workspace settings.
Running Flows & Scripts
Run a script or flow using wmill flow/script run u/username/path/to/script and
pass any inputs using --data + Inputs specified as a JSON string or a file
using @ <filename> or stdin using @-.
Curl-style syntax using -d @- for stdin or -d @<filename> is also supported.
Flow Steps and Logs will be streamed during execution automatically.
Pushing Resources, Scripts & More
The CLI can push specifications to a windmill instance. See the examples/ folder for formats.
Switch to a different workspace
wmill workspace switch <workspace_name>
Sync a workspace
Pull
wmill sync pull
Push
wmill sync push
We recommend using the --yaml option to use yaml instead of json as the encoding format. Yaml will be made the default soon.
Pushing individual files
You can push individual resources using
wmill <type> push <file_name> \<remote_name\>. This does not require a special
folder layout or file name, as this is given at runtime.
Listing
All commands support listing by just not providing a subcommand, ie
wmill script will result in a list of scripts. Some allow additional options,
learn about this by specifying --help.
User Management
You can add & remove users via wmill user add/remove, and list them using
wmill user
Pulling
You can pull the entire workspace using wmill pull
Completion
The CLI comes with completions out of the box via wmill completions <shell>.
(Via cliffy)
Bash
To enable bash completions add the following line to your ~/.bashrc:
source <(wmill completions bash)
Fish
To enable fish completions add the following line to your
~/.config/fish/config.fish:
source (wmill completions fish | psub)
Zsh
To enable zsh completions add the following line to your ~/.zshrc:
source <(wmill completions zsh)
Development
AI Guidance Variants
wmill init can now materialize alternate AI guidance bundles without changing
the generated defaults in the repo, but this is exposed as internal env-var
overrides rather than public CLI flags.
Examples:
WMILL_INIT_AI_SKILLS_SOURCE=/path/to/custom/skills wmill init --use-default
WMILL_INIT_AI_SKILLS_SOURCE=/path/to/custom/skills WMILL_INIT_AI_AGENTS_SOURCE=/path/to/AGENTS.md wmill init --use-default
WMILL_INIT_AI_SKILLS_SOURCE=/path/to/custom/skills WMILL_INIT_AI_CLAUDE_SOURCE=/path/to/CLAUDE.md wmill init --use-default
This is the same guidance-writing path used by the benchmark CLI under
ai_evals/, so the benchmark harness and wmill init now generate the same
project guidance shape:
AGENTS.mdCLAUDE.md.agents/skills/*.claude/skills/*
windmill-yaml-validator
wmill lint imports the sibling windmill-yaml-validator package from source rather than
from npm, so its schemas always match the OpenAPI specs of the current checkout. bun install regenerates them through this package's preinstall script; run it again after
editing openflow.openapi.yaml or backend/windmill-api/openapi.yaml:
npm --prefix ../windmill-yaml-validator run gen
Running Tests
Prerequisites:
- PostgreSQL running locally (default:
postgres://postgres:changeme@localhost:5432) - Rust toolchain installed
Run tests locally (full features):
bun test test/
Run tests in CI mode (minimal features, skips EE tests):
CI_MINIMAL_FEATURES=true bun test test/
| Variable | Description |
|---|---|
CI_MINIMAL_FEATURES |
Set to true to skip EE-dependent tests |
DATABASE_URL |
PostgreSQL connection string |
EE_LICENSE_KEY |
Enterprise license key for EE features |