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+# System Prompt And Skill Output Testing Plan
+
+## Goal
+
+Build a single testing strategy that answers one question reliably:
+
+> Given a user task, how good is the artifact produced by our AI system?
+
+This plan is intentionally focused on **black-box output evaluation**, not on unit testing frontend or CLI internals.
+
+The system under test is:
+
+- Frontend AI Chat in `script`, `flow`, and `app` modes
+- CLI local development experience driven by generated guidance and skills
+
+The artifact under test is:
+
+- Script code
+- Flow JSON / module structure
+- Raw app files and backend runnables
+- Files and project artifacts produced in a local CLI workspace
+
+## Non-Goals
+
+This plan does **not** treat the following as the main testing target:
+
+- Unit testing helper functions, stores, or tool wrapper internals
+- UI rendering behavior, DOM interactions, or component-level correctness
+- `wmill init` correctness as a standalone product area
+- Backend route correctness except where it affects prompt delivery or AI configuration
+
+Those may still need lightweight tests, but they are not the core of prompt reliability evaluation.
+
+## Core Principles
+
+### 1. Black-box evaluation only
+
+The runner should provide an input task to the real system setup, let it run, collect the final artifact, and score the result.
+
+### 2. Headless execution
+
+Frontend evaluation must be fully decoupled from the browser UI. It should exercise prompt assembly, tool selection, and tool execution logic without mounting Svelte components or clicking through the app.
+
+### 3. Real prompt environment
+
+All evals must use the same prompt-building path, tool definitions, and skill content that production uses, or a clearly defined variant of them.
+
+### 4. Artifact-first scoring
+
+The main score is based on the produced artifact, not on intermediate transcripts.
+
+### 5. Reliability over one-off success
+
+A prompt is not "good" because it passed once. Reliability means pass rate across repeated runs and across a representative case set.
+
+### 6. Shared corpus, separate adapters
+
+Frontend and CLI should share the same evaluation corpus format when possible, but each surface should have its own execution adapter.
+
+### 7. UI comes last
+
+The testing suite must exist and be trustworthy before building a studio UI on top of it.
+
+## Current State
+
+## Shared Prompt Source Of Truth
+
+The repo already has the right content split:
+
+- `system_prompts/` is the shared source of truth for core Windmill prompt content
+- frontend adds chat-specific tool instructions on top
+- CLI materializes guidance and skill content from generated outputs
+
+This is a strong foundation for a shared eval suite.
+
+## Frontend: What Exists Today
+
+The current frontend evals are already **mostly decoupled from the UI**.
+
+They currently:
+
+- run through the shared headless chat loop
+- use production prompt builders
+- use production tool definitions
+- use in-memory helper implementations instead of browser interactions
+
+This means the current flow and app evals are already closer to a proper black-box prompt harness than to a frontend UI test.
+
+That is the correct direction.
+
+## Frontend: What Is Missing
+
+### Coverage gaps
+
+- There is no equivalent eval suite for `script` mode.
+- Existing coverage is concentrated on `flow` and `app`.
+
+### Reliability gaps
+
+- Existing evals are mostly report-oriented rather than pass/fail oriented.
+- They write results and logs, but they do not yet act like a proper reliability gate.
+- They do not compute pass rate across repeated runs.
+
+### Prompt-iteration gaps
+
+- Testing a new prompt variant is possible in code, but not ergonomic enough.
+- Variants are still mostly defined in TypeScript, not in a prompt-candidate workflow that makes experimentation cheap.
+- There is no simple "baseline vs candidate" benchmark command that product or prompt authors can run repeatedly.
+
+### Artifact-validation gaps
+
+- The current flow and app helpers mock several effects instead of validating the resulting artifact deeply enough.
+- Linting and runnable validation are currently too lightweight in the eval path.
+- Datatable interactions are mocked rather than validated as output constraints.
+- The suite does not yet enforce a strong deterministic validator layer before using an LLM judge.
+
+### Corpus gaps
+
+- There is no single shared evaluation corpus for frontend surfaces.
+- Cases are not yet organized as a reusable benchmark set with metadata, tags, and expected constraints.
+- There is no explicit regression library built from real failures.
+
+### Reporting gaps
+
+- There is no stable result format for comparing runs over time.
+- There is no leaderboard view by prompt variant, provider, model, or surface.
+- There is no "worst failures first" report for debugging regressions.
+
+## Frontend: Perfect Testing Logic
+
+The perfect frontend testing logic is:
+
+### 1. Stay fully headless
+
+Do not mount the chat UI.
+
+Do not click through the frontend.
+
+Do not use Playwright for prompt evaluation.
+
+The runner should directly invoke:
+
+- the production system message builder
+- the production user message builder
+- the production tool list
+- the production chat loop
+
+This keeps the suite decorrelated from the frontend UI while still testing the real AI logic.
+
+### 2. Test the three frontend AI surfaces separately
+
+#### Script mode
+
+Input:
+
+- user prompt
+- optional initial script
+- optional context such as selected workspace runnables or DB references
+
+Output:
+
+- final script code
+
+Scoring:
+
+- deterministic validators first
+- LLM judge second
+
+Deterministic validators should include:
+
+- expected entrypoint present
+- syntax / parse validity
+- language-appropriate compile or lint check where feasible
+- required behaviors or structures present
+- forbidden patterns absent
+
+#### Flow mode
+
+Input:
+
+- user prompt
+- optional initial flow
+- optional schema
+- optional workspace context
+
+Output:
+
+- final flow definition
+
+Scoring:
+
+- flow JSON is structurally valid
+- expected module types exist
+- expected branches / loops / tools exist
+- schema shape matches required inputs
+- required data flow connections are present
+- LLM judge scores completeness and overall quality
+
+#### App mode
+
+Input:
+
+- user prompt
+- optional initial app
+- optional workspace context
+
+Output:
+
+- final frontend files
+- final backend runnables
+
+Scoring:
+
+- expected files and runnables exist
+- file structure is coherent
+- app bundle / lint checks pass where feasible in headless mode
+- required UI/backend behaviors are represented in the artifact
+- LLM judge scores completeness and product quality
+
+### 3. Use repeated runs, not single runs
+
+Each case should run more than once.
+
+Recommended starting point:
+
+- PR smoke run: 2 runs per case on a small curated subset
+- nightly reliability run: 5 to 10 runs per case on the full benchmark set
+
+Primary metric:
+
+- pass rate
+
+Secondary metrics:
+
+- average deterministic score
+- average judge score
+- worst-case judge score
+- latency
+- total tool calls
+
+### 4. Keep tool traces as diagnostics only
+
+Tool usage matters for debugging, but it should not be the primary score.
+
+The suite should record:
+
+- tool names
+- tool arguments
+- iteration count
+- model/provider
+
+But the main question remains:
+
+> Was the final artifact good?
+
+### 5. Make prompt variants easy to test
+
+Prompt candidates should not require editing test code.
+
+The suite should support a file-based prompt variant workflow.
+
+Example direction:
+
+- `ai_evals/variants/frontend/script/baseline.md`
+- `ai_evals/variants/frontend/script/candidate-a.md`
+- `ai_evals/variants/frontend/flow/baseline.md`
+- `ai_evals/variants/frontend/app/baseline.md`
+
+Each variant should be runnable side by side against the same case set.
+
+### 6. Separate benchmark cases from test code
+
+Benchmark cases should live in data files, not inline in test files.
+
+Each case should define:
+
+- surface
+- user prompt
+- initial artifact if any
+- required constraints
+- forbidden constraints
+- judge rubric
+- tags
+
+This makes the benchmark editable by prompt authors without changing runner logic.
+
+## CLI: What Exists Today
+
+The current CLI tests prove only one narrow property:
+
+> Given a prompt, does the model invoke the expected skill?
+
+That is useful as a smoke signal, but it is far from sufficient for output evaluation.
+
+The current CLI setup also depends on manual preparation of a `.claude/skills` folder, which makes repeated benchmarking and prompt iteration much harder than necessary.
+
+## CLI: What Is Missing
+
+### Output-evaluation gap
+
+- The current suite does not score the artifact produced by the CLI workflow.
+- It only checks whether a skill was invoked.
+- It does not verify that the resulting files are good.
+
+### Automation gap
+
+- The current setup requires manual copying of generated skills into a test folder.
+- That makes the suite too fragile and too manual for rapid prompt iteration.
+
+### Reliability gap
+
+- There is no repeated-run measurement.
+- There is no pass-rate metric.
+- There is no baseline vs candidate comparison workflow.
+
+### Prompt-variant gap
+
+- There is no first-class way to test alternate skill bundles or alternate generated guidance.
+- There is no clean candidate flow for "I changed skill content, show me whether reliability improved."
+
+### Corpus gap
+
+- CLI cases are not aligned with frontend benchmark cases.
+- There is no shared benchmark language describing the task, initial state, and expected artifact.
+
+### Reporting gap
+
+- There is no stable output report for artifact comparison.
+- There is no failure clustering by skill bundle, task family, or model.
+
+## CLI: Perfect Testing Logic
+
+The perfect CLI testing logic is:
+
+### 1. Evaluate the final artifact, not the skill invocation
+
+Skill invocation should be kept as diagnostic metadata only.
+
+The primary output should be the files produced in a temporary workspace.
+
+Example CLI artifacts:
+
+- generated script files
+- generated flow files
+- raw app project files
+- schedule / trigger config files
+- AGENTS / guidance files only when they are directly relevant to the task
+
+### 2. Create the workspace automatically
+
+The runner should create a fresh temporary project for every case.
+
+It should seed that workspace with:
+
+- initial files for the benchmark case
+- the current generated CLI guidance and skills
+- any fixture data required by the task
+
+It should never depend on a manually maintained test folder.
+
+### 3. Materialize the exact skill bundle under test
+
+The runner should be able to test:
+
+- the current production skill bundle
+- a candidate skill bundle built from prompt changes
+
+For CLI, a "prompt variant" is effectively a skill-bundle variant.
+
+That means the suite should support alternate generated skill content without requiring ad hoc manual copies.
+
+### 4. Score the final workspace
+
+The scoring approach should match the frontend philosophy:
+
+- deterministic validators first
+- LLM judge second
+
+Deterministic validators for CLI should include:
+
+- expected files created
+- expected file names and locations
+- required content patterns present
+- expected artifact type produced
+- optional parse / lint / compile validation where feasible
+
+### 5. Run repeated benchmarks
+
+The CLI should use the same reliability logic as frontend:
+
+- benchmark set
+- repeated runs
+- pass rate
+- baseline vs candidate comparison
+
+### 6. Keep skill traces as diagnostics
+
+Record:
+
+- invoked skills
+- order of invocation
+- turns
+- file changes
+
+But do not let that replace artifact evaluation.
+
+## Perfect Shared Benchmark Model
+
+The frontend and CLI should share the same benchmark concept.
+
+Each evaluation case should define:
+
+- `id`
+- `surface`
+- `user_prompt`
+- `initial_state`
+- `workspace_context`
+- `artifact_checks`
+- `judge_rubric`
+- `tags`
+
+The same task should be runnable on multiple surfaces when it makes sense.
+
+This gives direct comparability between:
+
+- frontend script vs CLI script
+- frontend flow vs CLI flow
+- frontend app vs CLI app
+
+## Recommended Benchmark Categories
+
+The first benchmark set should be broad, but not huge.
+
+Recommended initial size:
+
+- 20 to 30 core cases
+
+Recommended categories:
+
+- from-scratch script creation
+- script modification
+- from-scratch flow creation
+- flow modification
+- from-scratch raw app creation
+- raw app modification
+- reuse of workspace assets
+- tasks requiring datatable awareness
+- tasks requiring constraints or edge-case handling
+- known regressions from real failures
+
+Every category should contain both:
+
+- "easy success" cases
+- "high ambiguity" cases
+
+This is essential for measuring reliability rather than only measuring best-case demos.
+
+## Scoring Model
+
+The suite should use three layers.
+
+## Layer 1: Deterministic Validators
+
+This is the hard gate.
+
+Examples:
+
+- parse succeeds
+- artifact shape is valid
+- required entrypoint exists
+- expected files exist
+- required module types exist
+- expected inputs / schema fields exist
+- forbidden patterns are absent
+
+If layer 1 fails, the run is a failure.
+
+## Layer 2: Task-Specific Validators
+
+These are stronger artifact checks derived from the benchmark case.
+
+Examples:
+
+- flow contains a loop and a conditional branch
+- app includes a reset button path and backend wiring
+- script performs the requested transformation
+
+These should still be deterministic whenever possible.
+
+## Layer 3: LLM Judge
+
+Use an LLM judge only after deterministic validation.
+
+The judge should answer:
+
+- Did the artifact satisfy the request?
+- Is it complete?
+- Is it coherent for Windmill?
+- How close is it to the intended solution?
+
+The judge score is valuable, but it should not be the only oracle.
+
+## Reliability Metrics
+
+Every prompt or skill candidate should be reported with:
+
+- total cases
+- passes
+- pass rate
+- average judge score
+- median judge score
+- worst-case judge score
+- average latency
+- average turns
+
+Per-case results should also be retained.
+
+This is the minimum needed to compare:
+
+- baseline vs candidate
+- provider vs provider
+- frontend vs CLI
+
+## Proposed Suite Architecture
+
+The suite should be built in four layers.
+
+## Layer 1: Benchmark Data
+
+Purpose:
+
+- define the cases once
+
+Contents:
+
+- case files
+- reusable initial fixtures
+- evaluation metadata
+
+## Layer 2: Surface Adapters
+
+Purpose:
+
+- run a case against one surface
+
+Adapters:
+
+- frontend-script adapter
+- frontend-flow adapter
+- frontend-app adapter
+- CLI adapter
+
+Responsibilities:
+
+- prepare the correct prompt environment
+- prepare the initial artifact state
+- run the real model loop
+- return the final artifact plus diagnostics
+
+## Layer 3: Scoring And Reporting
+
+Purpose:
+
+- evaluate the final artifact
+- aggregate repeated runs
+- compare variants
+
+Responsibilities:
+
+- deterministic validation
+- LLM judging
+- pass/fail computation
+- result serialization
+- comparison reports
+
+## Layer 4: UI Studio
+
+Purpose:
+
+- provide a user interface for the exact same runner
+
+Important rule:
+
+The UI must not define its own execution semantics.
+
+It must only be a frontend over the same suite used in CI and local benchmarking.
+
+## Proposed Development Order
+
+### Phase 1: Stabilize the benchmark model
+
+Deliverables:
+
+- shared case schema
+- shared result schema
+- initial core benchmark set
+
+### Phase 2: Finish the frontend black-box harness
+
+Deliverables:
+
+- convert current flow and app evals into proper scored reliability tests
+- add script eval support
+- add repeated-run support
+- add prompt-variant loading from files
+
+### Phase 3: Replace the CLI smoke suite with real artifact evaluation
+
+Deliverables:
+
+- temp-workspace runner
+- automatic skill-bundle materialization
+- artifact scoring
+- repeated-run support
+
+### Phase 4: Add shared reporting
+
+Deliverables:
+
+- baseline vs candidate reports
+- pass-rate summaries
+- worst-failure reports
+- stored run history
+
+### Phase 5: Add CI tiers
+
+Deliverables:
+
+- fast PR smoke benchmark
+- fuller nightly benchmark
+- manual benchmark mode for prompt authors
+
+### Phase 6: Build the UI studio
+
+Deliverables:
+
+- run selector
+- variant selector
+- per-case comparison view
+- artifact diff view
+- reliability dashboard
+
+This phase comes last because the UI is only valuable once the underlying suite is stable and trusted.
+
+## Proposed Prompt Variant Workflow
+
+The suite should make it cheap to test new prompt candidates.
+
+Recommended workflow:
+
+1. Edit or add a candidate prompt file.
+2. Run the benchmark against baseline and candidate.
+3. Compare pass rate and score.
+4. Inspect worst regressions first.
+5. Promote only if the candidate improves the benchmark materially.
+
+For CLI, the same workflow applies, but the tested unit is the generated skill bundle rather than a single chat system prompt.
+
+## Suggested Repository Direction
+
+This plan does not require the UI studio to exist first.
+
+A reasonable repo structure would be:
+
+```text
+ai_evals/
+  cases/
+  fixtures/
+  variants/
+    frontend/
+      script/
+      flow/
+      app/
+    cli/
+  results/        # gitignored
+  adapters/
+  scoring/
+  reports/
+```
+
+The exact folder names can change, but the architectural split should remain.
+
+## What "Done" Looks Like
+
+This project is successful when all of the following are true:
+
+- frontend prompt behavior is tested headlessly and independently from the UI
+- CLI local-dev behavior is tested by evaluating the final files it produces
+- benchmark cases are shared where possible between frontend and CLI
+- prompt and skill candidates can be tested without editing test code
+- reliability is reported as pass rate over repeated runs
+- baseline vs candidate comparisons are easy to run and inspect
+- the UI studio is only a thin interface over the same trusted runner
+
+## Final Recommendation
+
+The current frontend evals should be treated as a useful starting point, not the finished solution.
+
+They already prove that the repo can test AI behavior without coupling to the browser UI.
+
+The main work now is:
+
+- make frontend evals complete and reliability-oriented
+- replace CLI invocation checks with artifact evaluation
+- unify both under one benchmark model
+- build the UI only after the suite is strong enough to stand on its own