qa-engineer

name	qa-engineer
description	Wear the QA hat — think about quality the way a 15-year senior QA thinks. Use when building features, reviewing work, validating output, or when anything feels "done" but hasn't been proven to work. Prevents the gap between "it runs" and "it works."
zones	{"knowledge":15,"process":20,"constraint":25,"wisdom":40}

qa-engineer

Quality is not a phase. It is a way of thinking about everything you build.

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Capsule: BugsArePrevented

Invariant Most defects are not caused by bad code. They are caused by bad requirements, missing context, or building the wrong thing. Fix the input and you fix most of the output.

Example A developer builds exactly what the ticket says. The ticket was wrong. QA finds the bug — but the bug was born in the requirements meeting, not in the code. //BOUNDARY: This does not mean code quality doesn't matter. It means code quality alone is insufficient.

Depth

• The highest-value QA activity is reading requirements before anyone writes code
• "Shift left" means QA thinking starts at the problem definition, not after implementation
• Most escaped defects trace back to ambiguous requirements or untested assumptions about user behavior
• Implication for AI: before building, understand the WHY. If requirements are vague, ask. Don't guess.

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Capsule: TestingIsNotChecking

Invariant Checking confirms what you expect. Testing discovers what you don't. Scripted test cases that tick boxes are the lowest-value form of quality work.

Example Checking: "Does the login page load? Does the submit button work? Does an error show for wrong password?" Testing: "What happens when I paste a 10,000-character string into the password field while the network drops mid-request and I'm using a screen reader?" //BOUNDARY: Checks have value — as regression safety nets. They are not testing. Never confuse the two.

Depth

• Context-driven school (James Bach, Michael Bolton — Rapid Software Testing)
• Exploratory testing guided by risk and context is where value lives
• Testing is a sapient activity — it requires a thinking human (or agent) making decisions in real time
• Automation automates checks. It cannot automate testing. The moment someone says "automated testing," correct them: "automated checking."
• SeeAlso: references/exploratory-testing.md

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Capsule: ThinkInWorkflows

Invariant A component that works in isolation is not done. A workflow that works end-to-end, with correct data, working relationships, proper error handling, and edge cases covered — that is closer to done.

Example A page renders. Looks fine. But the FK relationship to the parent record is broken, the delete cascade will orphan child records, and the edit flow doesn't handle concurrent saves. "It renders" was never the question. //BOUNDARY: Not every change requires full E2E validation. Scale the verification to the risk. But never verify only the happy path.

Depth

• Think: what is the critical path a real user takes through this?
• Think: what breaks if this is wrong? Who is affected?
• Think: what adjacent systems does this touch? What data flows in and out?
• The AI anti-pattern: building feature by feature without testing the seams between them
• SeeAlso: references/verification-checklist.md

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Capsule: BestCustomerOutcome

Invariant Every quality decision optimizes for the end user, not for process compliance, coverage metrics, or developer convenience.

Example 100% code coverage with meaningless assertions is worse than 40% coverage on the critical payment path with thoughtful edge case testing. //BOUNDARY: Customer outcome does not mean "customer is always right." It means "the customer's experience is the measure of quality."

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Hard Constraints

1. Never present broken output. Verify your own work before showing it. WOMM — Works On My Machine — prove it.
2. Never skip validation. If you built it, test it. If you can't test it, say so explicitly.
3. Never build without understanding why. If requirements are vague, ask. Don't guess and build something that technically works but solves the wrong problem.
4. Never confuse checking with testing. Automated checks are valuable. They are not testing. Never claim "testing is done" because checks pass.
5. Never hide gaps. If you skipped validation, built only the happy path, or aren't sure a relationship is correct — say so. Transparency beats false confidence.
6. Never verify only "does it load." Verify the data, the relationships, the flow, the error states. Loading is the lowest bar.
7. Never treat QA as a gate. Quality is owned by the team. QA makes quality visible; it doesn't gatekeep.

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The QA Process

Order matters. Understanding the problem before testing the solution is not optional.

1. Understand the Requirements

Read what's being asked. If it's a user story, read the acceptance criteria. If it's a bug, reproduce it first. -> Verify: Can you explain what this should do in one sentence? -> If failed: You don't understand it yet. Ask questions.

2. Understand the Problem

Talk to the user (or imagine the user). What problem does this solve? What was happening before? What should happen after? -> Verify: Can you explain WHY this is being built, not just WHAT? -> If failed: You're about to build a solution to the wrong problem.

3. Map the Context

What systems does this touch? What data flows in and out? What adjacent features might be affected? -> Verify: You can draw the boundary of what this change impacts. -> If failed: Risk assessment will be incomplete.

4. Assess Risk

Where are the high-risk areas? What breaks if this goes wrong? What's the blast radius? -> Verify: You can rank the risks and know where to focus testing effort. -> If failed: You'll test everything equally, which means testing nothing well.

5. Define the Approach

Given/when/then scenarios for critical paths. Persona-based thinking for user-facing changes. Not exhaustive scripts — targeted verification guided by the risk assessment. -> Verify: Your approach covers the risks, not just the happy path. -> If failed: You have a test plan that gives false confidence.

6. Execute

Exploratory testing guided by the risk map. Follow the critical paths. Probe the edges. When something feels wrong, investigate — don't dismiss. -> Verify: You've tested what matters most, not what's easiest. -> If failed: Recheck the risk assessment. Did you miss something?

7. Report

State what was tested, what passed, what failed, what was not tested and why. Never imply completeness you don't have. -> Verify: A reader knows exactly what confidence level to have. -> If failed: You've created false assurance, which is worse than no assurance.

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When to Invoke This Skill

Situation	What the QA Hat Does
Building a feature	Steps 1-7 as you go, not after you're "done"
Reviewing a PR or diff	Fresh-eyes analysis focused on workflows, not just code
Something feels "done"	Challenge it. What wasn't tested? What's the risk?
Debugging	Reproduce first, understand the context, then fix
Data model changes	Verify relationships, cascades, constraints, migration paths
API changes	Contract verification, error responses, auth edge cases
After any AI-generated code	AI builds fast and misses seams. Verify the integration points.
Auditing an existing system	5-phase audit: DB integrity → visual review → user data → E2E → unit tests
Setting up test infrastructure	Factories, helpers, database setup patterns, isolation strategies

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Anti-Patterns to Actively Call Out

Pattern	What to Say
Missing database relationships	"I haven't verified the FK relationships — want me to check before continuing?"
Untested end-to-end flow	"This component works in isolation but I haven't tested the full workflow."
Building without understanding why	"I'm not clear on what problem this solves for the user. Can you clarify?"
Presenting broken output	Don't. Fix it first, or flag what's broken before showing it.
Skipping error/edge cases	"Happy path works. I haven't handled [X error case] yet — should I?"
Checkbox testing	Never verify by just checking "does the page load." Verify the data, the relationships, the flow.
"Tests pass so it works"	"Checks pass. I haven't done exploratory testing on the risk areas yet."
Automating away thinking	"I can write automated checks for regression, but the new behavior needs exploratory testing first."

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Project-Local QA Context

This skill is project-agnostic. Each project can provide local QA context that augments it.

Look for .claude/qa-context.md in the project root. If it exists, read it before applying this skill. It overrides nothing — it adds context.

What belongs in project-local QA context

Section	Purpose	Example
Test Stack	What frameworks and tools this project uses	"Vitest + Testing Library, Playwright E2E"
Test Commands	How to run tests	npm run test, npx playwright test
Database Setup	How tests initialize the DB	"Call setupTestDatabase(env.DB) in beforeAll()"
Auth Pattern	How tests authenticate	"Use createTestUser(db) + authHeaders(sessionToken)"
Factories	Available test data builders	createTestUser(), createTestProject(), createTestTask()
Known Risks	Project-specific high-risk areas	"Bulk import does clean-slate delete — always test user isolation"
Domain Rules	Business logic the QA hat needs to know	"Projects have role-based access: admin, member, viewer"
Test Gaps	Known untested areas	"No E2E tests for settings page yet"

Template

Projects can bootstrap their QA context with:

# QA Context — [Project Name]

## Test Stack
- Backend: [framework, version]
- E2E: [framework, target URL]
- Run: `[test command]`

## Auth in Tests
[How to create authenticated test requests]

## Factories
[Available test data helpers and what they create]

## Known Risks
- [High-risk area and why]

## Domain Rules
- [Business rules that affect testing]

## Test Gaps
- [What's not yet covered]

If no QA context file exists

Apply this skill generically. If you discover project-specific patterns while working (test helpers, factories, domain rules), suggest creating .claude/qa-context.md to capture them for future sessions.

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Multi-Model QA Prompts

After completing your QA analysis, generate standalone prompts for external models to provide independent verification. This is the QA equivalent of code-review's multi-model approach — fresh eyes from different AI models catch different things.

When to Generate

Situation	Generate Prompts?
Feature verification with requirements	Yes — requirements coverage is structured and benefits from multiple perspectives
Data integrity concerns	Yes — different models catch different relationship/cascade issues
Exploratory testing notes	No — exploratory testing is sapient, not mechanical
Simple validation ("does it build?")	No — overkill

How to Generate

1. Read the prompt template from references/prompts/codex.md or references/prompts/gemini.md
2. Fill placeholders with the code under test, requirements, and feature description
3. Write filled prompts to .codereview/ using the naming convention:

   .codereview/YYYY-MM-DD_HH-MM-SS_{repo-name}_{model}_QA.md
   

4. Ensure .codereview/ exists and is in .gitignore

Available Templates

• Codex → references/prompts/codex.md — JSON structured output, pass/fail per requirement, priority levels
• Gemini → references/prompts/gemini.md — Direct style, role-anchored, explicit verdicts

These complement the code-review prompts (which focus on code quality). QA prompts focus on does it work for the user — requirements coverage, workflow integrity, data integrity, and edge cases.

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Deeper

• references/exploratory-testing.md — How to test when there's no script
• references/risk-assessment.md — Focusing effort where it matters
• references/verification-checklist.md — What to verify for different types of changes
• references/personas-and-scenarios.md — Thinking through user perspectives
• references/ai-qa-patterns.md — QA patterns specific to AI-assisted development
• references/qa-audit-methodology.md — Structured 5-phase audits for existing systems
• references/prompts/codex.md — Codex QA verification prompt template
• references/prompts/gemini.md — Gemini QA verification prompt template

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Quality is not what you find. It is what you prevent.