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Before starting: Check for .agents/qa-project-context.md in the project root. It carries tech stack, test frameworks, naming conventions, selector strategy, and known risk areas that dramatically improve generated test quality.
The pipeline is the same for every input; only the Step 1 extraction emphasis changes. Jump to the matching row, then run Steps 2-7 unchanged.
| Input type | Step 1 extracts | Watch for |
|---|---|---|
| PRD / feature spec | Entities, business rules, acceptance criteria, NFRs, stated assumptions | Implicit requirements inferred from "seamless"/"fast" language |
| User story + AC | Each AC → ≥1 happy + ≥1 negative scenario | ACs that hide multiple behaviors in one line |
Code diff (git diff main...HEAD) | New/changed code paths, modified conditionals, removed behavior | Regression scope: test the changed paths, not the whole module |
| Bug report | Repro steps, expected vs actual, environment | Write a test asserting expected — fails now, passes after fix |
| OpenAPI / GraphQL SDL | Endpoints, schemas, required fields, enums, auth | Validation, auth-failure, and edge cases per endpoint, not just 200s |
Playwright projects also pick an agent integration mode — see Discovery Q2.
Check .agents/qa-project-context.md first — if it exists, use it and skip anything already answered there. Then clarify:
What is the input source? PRD / spec, user story + AC, code diff, bug report, or API schema. Determines Step 1 extraction emphasis (see Quick Route). For an LLM/AI feature spec, stop — generate eval datasets in ai-system-testing, not Playwright specs.
What is the target test framework, and (for Playwright) which agent integration mode?
APIRequestContext, Supertest, requests.npx playwright init-agents --loop=claude scaffolds planner/generator/healer agents into .claude/agents/ as markdown. They are interactive dev tools that produce standard Playwright tests which run unchanged in CI. Token-efficient; runs inside the agent's loop.npx @playwright/mcp@latest): higher overhead, right when the agent must drive a live browser interactively over a long session.What project context is available? Existing test patterns, Page Objects / helpers, data factories / fixtures, CI constraints (timeout, parallelism). More context = less cleanup.
What is the review workflow? Full pipeline → human review → merge (default); scenarios only → human writes code; or code → human refines iteratively.
What domain knowledge is needed? Regulated industry (healthcare, finance) compliance, domain invariants (money never negative, appointments cannot overlap), known risk areas from past incidents.
Pipeline before code. Never generate test code before establishing what to test, why, and how to verify it. The seven-step pipeline exists to prevent premature code generation that targets the wrong things.
Structured intermediates are the product. The assumptions document, coverage matrix, and oracle definitions are more valuable than the test code itself. They are reviewable, traceable, and reusable.
Separate what from how. Scenario generation (what to test) and oracle design (how to verify) are distinct cognitive tasks. Mixing them produces scenarios biased toward what is easy to assert, with assertions tacked on as afterthoughts.
AI generates the first draft; a human reviews and refines. Never ship AI-generated tests without human review. The AI accelerates — it does not replace judgment.
Context is everything. Feed the LLM your conventions, existing patterns, selector strategy, and data setup. The more context, the less cleanup.
Quality over quantity. Each test has a maintenance cost. Focus on critical paths, complex logic, and known risk areas — not test count.
Mandatory workflow — agents MUST follow this order:
Step 1: Extract → Requirements, entities, business rules from input
Step 2: Analyze → Risks, invariants, edge cases, ambiguities
Step 3: Map → Coverage matrix (requirement → scenario → priority)
Step 4: Generate → Candidate scenarios (happy + boundary + negative + security + a11y)
Step 5: Design → Assertions and oracles SEPARATELY from scenarios
Step 6: Code → Test code (only after all above exist)
Step 7: Review → Human review with traceability back to source
Full prompt templates for every step (extraction, risk analysis, scenario, oracle, code) live in references/prompt-patterns.md. Below is the shape of each step's output.
Parse the input into structured elements: Entities (with roles/states/attributes), Business Rules (numbered), Explicit Requirements ([REQ-N], stated in source), and Implicit Requirements ([IMP-N], inferred — flag every one for human confirmation). Separating explicit from inferred is the rule that prevents testing assumptions as if they were specifications.
Derive what can go wrong, what must always be true, and where the source is silent.
Risk | Likelihood | Impact | Source Requirement (e.g. race condition on stock decrement, email delay > 30s).stock >= 0, order total = sum(items) + tax + shipping, user sees only their own orders.The single most important artifact — it prevents both gaps and duplicates. Map every requirement to scenarios with category, priority, and oracle type:
| Requirement | Scenario | Category | Priority | Oracle Type |
|---|---|---|---|---|
| REQ-1 | Add single item to empty cart | Happy path | P0 | State: cart count = 1 |
| REQ-1 | Add out-of-stock item | Negative | P0 | UI: error message, cart unchanged |
| REQ-2 | Complete checkout with valid card | Happy path | P0 | State: order created, stock decremented |
| REQ-2 | Two users checkout last item | Race condition | P1 | One succeeds, one gets stock error |
| INV-1 | Stock never goes negative | Invariant | P0 | Data: stock >= 0 after any operation |
After building it, verify: every requirement has ≥1 happy and ≥1 negative scenario; every invariant has a direct test; every Step-2 risk has a scenario; no two rows test the same thing.
For each matrix row, write the full scenario in Given/When/Then with explicit test-data requirements (Given: user with 99 items in cart (max 100); When: adds one more; Then: count = 100). Cover these categories systematically:
| Category | Description |
|---|---|
| Happy path | The user does exactly what the feature is designed for |
| Boundary | Edge of valid input ranges — use the BOUNDARIES framework (references/prompt-patterns.md) |
| Negative | Invalid inputs, unauthorized actions |
| Security | Auth bypass, injection, privilege escalation |
| Accessibility | Screen reader, keyboard-only, contrast |
| State transition | Valid and invalid moves between states |
| Concurrency | Two users acting simultaneously |
Deliberately separate from Step 4. Scenarios describe behavior; oracles describe how to verify it. For each scenario, define oracles across categories — a single assertion is rarely enough to prove a behavior:
| Oracle category | Asserts | Example |
|---|---|---|
| UI state | Visible text / element state | cart badge toHaveText('1') |
| Data | Persisted state via API/DB | GET /api/cart returns 1 item, correct total |
| Negative | What should NOT happen | no error toast; no navigation away |
| Side effect | Async/external outcomes | analytics add_to_cart fired; email in inbox < 30s |
Oracle quality rules: assert business outcomes not implementation details; use the most specific assertion available (toHaveText('$29.99'), not toBeTruthy()); include negative assertions; verify data integrity, not just UI; assert accessibility (focus management, live-region announcements).
Only after Steps 1-5 produce reviewed artifacts. Code is a mechanical translation of scenarios + oracles into framework syntax, with traceability comments linking back to the requirement and scenario:
/**
* Scenario: SC-001 — Add single item to empty cart
* Requirement: REQ-1 (User can add items to cart)
* Priority: P0
*/
test('add single item to empty cart', async ({ page, testProduct }) => {
await page.goto(`/products/${testProduct.id}`); // Given
await page.getByRole('button', { name: 'Add to cart' }).click(); // When
await expect(page.getByTestId('cart-badge')).toHaveText('1'); // Then
await expect(page.getByTestId('error-toast')).not.toBeVisible(); // Negative oracle
});
Code generation rules: match project conventions (from qa-project-context.md); reuse existing Page Objects, fixtures, and data factories; include traceability comments (Scenario: SC-XXX, Requirement: REQ-XX); follow the project's selector strategy; put setup/teardown in fixtures, not inline.
Not optional — a mandatory pipeline step. This reviews the tests this pipeline just generated, before they merge. (To audit a pre-existing suite you did not just generate, use ai-qa-review instead.) Run every generated test against this checklist:
name: ai-test-generation description: >- Use AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context. license: MIT metadata: author: kindlmann version: "2.0" category: ai-qa
---
name: ai-test-generation
description: >-
Use AI to write NEW test code from specs, PRDs, user stories, code diffs, bug
reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk
analysis → coverage matrix → scenario generation → oracle design → test code →
human review, with guardrails against hallucinated APIs and weak assertions.
Use when: "generate tests from spec," "tests from PRD," "tests from user story,"
"auto-generate test cases," "AI write tests for me."
Not for: testing AI/LLM features in your product — use ai-system-testing.
Not for: auditing a pre-existing test suite you did not just generate — use
ai-qa-review (Step 7 here only reviews tests THIS pipeline produced).
Related: playwright-automation, unit-testing, api-testing, qa-project-context.
license: MIT
metadata:
author: kindlmann
version: "2.0"
category: ai-qa
---
<objective>
LLMs will happily emit fifty plausible-looking tests that assert nothing, target endpoints that do not exist, and duplicate each other. This skill is a staged pipeline that forces structured intermediates — assumptions, coverage matrix, oracle definitions — out of the model BEFORE any test code, so what you get is traceable, reviewable, and grounded in the real codebase instead of ad-hoc generated noise.
**Before starting:** Check for `.agents/qa-project-context.md` in the project root. It carries tech stack, test frameworks, naming conventions, selector strategy, and known risk areas that dramatically improve generated test quality.
</objective>
## Quick Route
The pipeline is the same for every input; only the Step 1 extraction emphasis changes. Jump to the matching row, then run Steps 2-7 unchanged.
| Input type | Step 1 extracts | Watch for |
|------------|-----------------|-----------|
| PRD / feature spec | Entities, business rules, acceptance criteria, NFRs, stated assumptions | Implicit requirements inferred from "seamless"/"fast" language |
| User story + AC | Each AC → ≥1 happy + ≥1 negative scenario | ACs that hide multiple behaviors in one line |
| Code diff (`git diff main...HEAD`) | New/changed code paths, modified conditionals, removed behavior | Regression scope: test the changed paths, not the whole module |
| Bug report | Repro steps, expected vs actual, environment | Write a test asserting *expected* — fails now, passes after fix |
| OpenAPI / GraphQL SDL | Endpoints, schemas, required fields, enums, auth | Validation, auth-failure, and edge cases per endpoint, not just 200s |
Playwright projects also pick an agent integration mode — see Discovery Q2.
## Discovery Questions
Check `.agents/qa-project-context.md` first — if it exists, use it and skip anything already answered there. Then clarify:
1. **What is the input source?** PRD / spec, user story + AC, code diff, bug report, or API schema. Determines Step 1 extraction emphasis (see Quick Route). For an LLM/AI feature spec, stop — generate eval datasets in `ai-system-testing`, not Playwright specs.
2. **What is the target test framework, and (for Playwright) which agent integration mode?**
- **E2E:** Playwright (preferred), Cypress. **Unit:** Jest, Vitest, pytest. **API:** Playwright `APIRequestContext`, Supertest, requests.
- **Playwright CLI + agents** (recommended for Claude Code / Codex / Cursor): `npx playwright init-agents --loop=claude` scaffolds planner/generator/healer agents into `.claude/agents/` as markdown. They are interactive dev tools that produce standard Playwright tests which run unchanged in CI. Token-efficient; runs inside the agent's loop.
- **Playwright MCP** (`npx @playwright/mcp@latest`): higher overhead, right when the agent must *drive* a live browser interactively over a long session.
- **Neither** — hand-write tests using AI as a scratch-pad helper.
3. **What project context is available?** Existing test patterns, Page Objects / helpers, data factories / fixtures, CI constraints (timeout, parallelism). More context = less cleanup.
4. **What is the review workflow?** Full pipeline → human review → merge (default); scenarios only → human writes code; or code → human refines iteratively.
5. **What domain knowledge is needed?** Regulated industry (healthcare, finance) compliance, domain invariants (money never negative, appointments cannot overlap), known risk areas from past incidents.
## Core Principles
1. **Pipeline before code.** Never generate test code before establishing what to test, why, and how to verify it. The seven-step pipeline exists to prevent premature code generation that targets the wrong things.
2. **Structured intermediates are the product.** The assumptions document, coverage matrix, and oracle definitions are more valuable than the test code itself. They are reviewable, traceable, and reusable.
3. **Separate what from how.** Scenario generation (what to test) and oracle design (how to verify) are distinct cognitive tasks. Mixing them produces scenarios biased toward what is easy to assert, with assertions tacked on as afterthoughts.
4. **AI generates the first draft; a human reviews and refines.** Never ship AI-generated tests without human review. The AI accelerates — it does not replace judgment.
5. **Context is everything.** Feed the LLM your conventions, existing patterns, selector strategy, and data setup. The more context, the less cleanup.
6. **Quality over quantity.** Each test has a maintenance cost. Focus on critical paths, complex logic, and known risk areas — not test count.
## The Pipeline
**Mandatory workflow — agents MUST follow this order:**
```
Step 1: Extract → Requirements, entities, business rules from input
Step 2: Analyze → Risks, invariants, edge cases, ambiguities
Step 3: Map → Coverage matrix (requirement → scenario → priority)
Step 4: Generate → Candidate scenarios (happy + boundary + negative + security + a11y)
Step 5: Design → Assertions and oracles SEPARATELY from scenarios
Step 6: Code → Test code (only after all above exist)
Step 7: Review → Human review with traceability back to source
```
Full prompt templates for every step (extraction, risk analysis, scenario, oracle, code) live in `references/prompt-patterns.md`. Below is the shape of each step's output.
### Step 1: Extract Requirements and Entities
Parse the input into structured elements: **Entities** (with roles/states/attributes), **Business Rules** (numbered), **Explicit Requirements** (`[REQ-N]`, stated in source), and **Implicit Requirements** (`[IMP-N]`, inferred — flag every one for human confirmation). Separating explicit from inferred is the rule that prevents testing assumptions as if they were specifications.
### Step 2: Risk Analysis and Invariants
Derive what can go wrong, what must always be true, and where the source is silent.
- **Risks** — table of `Risk | Likelihood | Impact | Source Requirement` (e.g. race condition on stock decrement, email delay > 30s).
- **Invariants** (must ALWAYS hold) — `stock >= 0`, `order total = sum(items) + tax + shipping`, `user sees only their own orders`.
- **Ambiguities** (need human answers) — "Does free shipping apply before or after discount codes?" Capture these explicitly; do not silently pick one.
- **Edge cases derived from risks** — two users buy the last item, payment succeeds but email service is down.
### Step 3: Coverage Matrix
The single most important artifact — it prevents both gaps and duplicates. Map every requirement to scenarios with category, priority, and oracle type:
| Requirement | Scenario | Category | Priority | Oracle Type |
|-------------|----------|----------|----------|-------------|
| REQ-1 | Add single item to empty cart | Happy path | P0 | State: cart count = 1 |
| REQ-1 | Add out-of-stock item | Negative | P0 | UI: error message, cart unchanged |
| REQ-2 | Complete checkout with valid card | Happy path | P0 | State: order created, stock decremented |
| REQ-2 | Two users checkout last item | Race condition | P1 | One succeeds, one gets stock error |
| INV-1 | Stock never goes negative | Invariant | P0 | Data: stock >= 0 after any operation |
After building it, verify: every requirement has ≥1 happy and ≥1 negative scenario; every invariant has a direct test; every Step-2 risk has a scenario; no two rows test the same thing.
### Step 4: Generate Candidate Scenarios
For each matrix row, write the full scenario in Given/When/Then with explicit test-data requirements (`Given: user with 99 items in cart (max 100); When: adds one more; Then: count = 100`). Cover these categories systematically:
| Category | Description |
|----------|-------------|
| Happy path | The user does exactly what the feature is designed for |
| Boundary | Edge of valid input ranges — use the BOUNDARIES framework (`references/prompt-patterns.md`) |
| Negative | Invalid inputs, unauthorized actions |
| Security | Auth bypass, injection, privilege escalation |
| Accessibility | Screen reader, keyboard-only, contrast |
| State transition | Valid and invalid moves between states |
| Concurrency | Two users acting simultaneously |
### Step 5: Design Assertions and Oracles
**Deliberately separate from Step 4.** Scenarios describe behavior; oracles describe how to verify it. For each scenario, define oracles across categories — a single assertion is rarely enough to prove a behavior:
| Oracle category | Asserts | Example |
|-----------------|---------|---------|
| UI state | Visible text / element state | `cart badge toHaveText('1')` |
| Data | Persisted state via API/DB | `GET /api/cart` returns 1 item, correct total |
| Negative | What should NOT happen | no error toast; no navigation away |
| Side effect | Async/external outcomes | analytics `add_to_cart` fired; email in inbox < 30s |
**Oracle quality rules:** assert business outcomes not implementation details; use the most specific assertion available (`toHaveText('$29.99')`, not `toBeTruthy()`); include negative assertions; verify data integrity, not just UI; assert accessibility (focus management, live-region announcements).
### Step 6: Generate Test Code
**Only after Steps 1-5 produce reviewed artifacts.** Code is a mechanical translation of scenarios + oracles into framework syntax, with traceability comments linking back to the requirement and scenario:
```typescript
/**
* Scenario: SC-001 — Add single item to empty cart
* Requirement: REQ-1 (User can add items to cart)
* Priority: P0
*/
test('add single item to empty cart', async ({ page, testProduct }) => {
await page.goto(`/products/${testProduct.id}`); // Given
await page.getByRole('button', { name: 'Add to cart' }).click(); // When
await expect(page.getByTestId('cart-badge')).toHaveText('1'); // Then
await expect(page.getByTestId('error-toast')).not.toBeVisible(); // Negative oracle
});
```
**Code generation rules:** match project conventions (from `qa-project-context.md`); reuse existing Page Objects, fixtures, and data factories; include traceability comments (`Scenario: SC-XXX`, `Requirement: REQ-XX`); follow the project's selector strategy; put setup/teardown in fixtures, not inline.
### Step 7: Human Review
Not optional — a mandatory pipeline step. This reviews **the tests this pipeline just generated**, before they merge. (To audit a pre-existing suite you did not just generate, use `ai-qa-review` instead.) Run every generated test against this checklist:
- [ ] **Traces to requirement:** test → scenario → coverage row → requirement is followable.
- [ ] **Tests behavior, not implementation:** survives a harmless refactor.
- [ ] **Correct abstraction level:** right test type (unit vs integration vs E2E).
- [ ] **Test naming and readability:** the test name states the behavior; a reader sees intent without decoding the body.
- [ ] **Test isolation / no shared state:** the test creates and cleans up its own data, holds no order dependency on sibling tests, and passes when run alone or in any oSkill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
61/100
Promising
Trust
60/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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]
},
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"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 108 stars, 22 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access"
]
},
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},
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"high-compliance environments without internal security review",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision."
],
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"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
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"Audit: 71/100 Needs review",
"Safety: 23/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
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"install_command": "npx skills add petrkindlmann/qa-skills --skill ai-test-generation",
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},
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"setup_required"
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"payload_template": {
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"task": "Use ai-test-generation in an agent workflow",
"agent": "codex",
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"workspace": "sandbox",
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},
"endpoints": {
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"api": "https://www.openagentskill.com/api/agent/skills/petrkindlmann-ai-test-generation",
"audit": "https://www.openagentskill.com/skills/petrkindlmann-ai-test-generation/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=petrkindlmann-ai-test-generation&task=Use%20ai-test-generation%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-test-generation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-test-generation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/petrkindlmann-ai-test-generation/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/petrkindlmann-ai-test-generation"
}
}Listing source
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Audit
71/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.