Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Comprehensive quality assurance workflow that validates implementation completeness and correctness, then iterates through fix cycles until approval. You are the last line of defense before shipping.
Core principle: You are the last line of defense. If you approve, the feature ships. Be thorough.
Always:
Exceptions:
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Approving before checking all acceptance criteria | Ships bugs disguised as features; breaks the quality contract | Verify every criterion in the spec before writing the verdict |
| Writing vague issue reports ("tests fail", "broken") | Developer cannot reproduce or fix what is not precisely described | Include file path, line number, exact error message, and reproduction steps |
| Signing off with known failing tests | Failing tests are documented bugs being shipped to production | All tests must pass; if tests are wrong, fix them first and document the change |
| Only running new tests, not the full regression suite | New code breaking old functionality is invisible without full suite | Always run full suite with --coverage; regressions block approval |
| Fixing more than QA found to "clean things up" | Over-fixing introduces new bugs and scope creep into the fix loop | Apply minimal changes; fix only what QA identified, nothing more |
Every acceptance criterion must be verified before approval.
# Read the spec (your source of truth for requirements)
cat .claude/context/specs/[task-name]-spec.md
# Read any previous QA reports
cat .claude/context/reports/qa/qa-report.md 2>/dev/null || echo "No previous report"
# See what files were changed
git diff main...HEAD --name-status
# Read QA acceptance criteria from spec
grep -A 100 "## QA Acceptance" spec.md
# Check git log for implementation commits
git log --oneline main..HEAD
# Verify expected files were modified
git diff main...HEAD --name-only
STOP if implementation is not complete. QA runs after implementation.
# Start services as needed
npm run dev # or appropriate command
# Verify services are running
curl http://localhost:3000/health 2>/dev/null || echo "Service not responding"
Wait for all services to be healthy before proceeding.
Run all unit tests for affected areas:
# Run test suite
npm test
# or
pytest
# or
go test ./...
Document results:
UNIT TESTS:
- [area-name]: PASS/FAIL (X/Y tests)
Run integration tests if applicable:
# Run integration test suite
npm run test:integration
Document results:
INTEGRATION TESTS:
- [test-name]: PASS/FAIL
If E2E tests exist:
# Run E2E test suite
npm run test:e2e
Document results:
E2E TESTS:
- [flow-name]: PASS/FAIL
For each acceptance criterion in the spec:
MANUAL VERIFICATION:
- [Criterion 1]: PASS/FAIL
- Evidence: [what you observed]
- [Criterion 2]: PASS/FAIL
- Evidence: [what you observed]
Check for common vulnerabilities:
# Look for security issues
grep -r "eval(" --include="*.js" --include="*.ts" . 2>/dev/null
grep -r "innerHTML" --include="*.js" --include="*.ts" . 2>/dev/null
grep -r "dangerouslySetInnerHTML" --include="*.tsx" --include="*.jsx" . 2>/dev/null
# Check for hardcoded secrets
grep -rE "(password|secret|api_key|token)\s*=\s*['\"][^'\"]+['\"]" . 2>/dev/null
Verify code follows established patterns:
# Compare new code to existing patterns
# Read pattern files, compare structure
Document findings:
CODE REVIEW:
- Security issues: [list or "None"]
- Pattern violations: [list or "None"]
- Code quality: PASS/FAIL
Run full test suite to catch regressions:
# Run ALL tests, not just new ones
npm test -- --coverage
Verify key existing functionality still works.
REGRESSION CHECK:
- Full test suite: PASS/FAIL (X/Y tests)
- Existing features verified: [list]
- Regressions found: [list or "None"]
# QA Validation Report
**Task**: [task-name]
**Date**: [timestamp]
## Summary
| Category | Status | Details |
| ------------------- | --------- | ----------- |
| Unit Tests | PASS/FAIL | X/Y passing |
| Integration Tests | PASS/FAIL | X/Y passing |
| E2E Tests | PASS/FAIL | X/Y passing |
| Manual Verification | PASS/FAIL | [summary] |
| Security Review | PASS/FAIL | [summary] |
| Pattern Compliance | PASS/FAIL | [summary] |
| Regression Check | PASS/FAIL | [summary] |
## Issues Found
### Critical (Blocks Sign-off)
1. [Issue description] - [File/Location]
### Major (Should Fix)
1. [Issue description] - [File/Location]
### Minor (Nice to Fix)
1. [Issue description] - [File/Location]
## Verdict
**SIGN-OFF**: [APPROVED / REJECTED]
**Reason**: [Explanation]
**Next Steps**:
- [If approved: Ready for merge]
- [If rejected: List of fixes needed]
Save report to .claude/context/reports/qa/qa-report.md
=== QA VALIDATION COMPLETE ===
Status: APPROVED
All acceptance criteria verified:
- Unit tests: PASS
- Integration tests: PASS
- Manual verification: PASS
- Security review: PASS
- Regression check: PASS
The implementation is production-ready.
Ready for merge.
Create fix request and proceed to Part 2.
# Read the QA report with issues
cat .claude/context/reports/qa/qa-report.md
# Identify issues to fix
grep -A 50 "## Issues Found" .claude/context/reports/qa/qa-report.md
Extract from report:
Create a checklist from the QA report:
FIXES REQUIRED:
1. [Issue Title]
- Location: [file:line]
- Problem: [description]
- Fix: [what to do]
- Verify: [how to check]
2. [Issue Title]
...
You must address EVERY issue.
For each issue:
Follow these rules:
After all fixes are applied:
# Run the full test suite
npm test
# Run specific tests that were failing
[failed test commands from QA report]
All tests must pass before proceeding.
Before requesting re-review, verify each fix:
SELF-VERIFICATION:
[ ] Issue 1: [title] - FIXED
- Verified by: [how you verified]
[ ] Issue 2: [title] - FIXED
- Verified by: [how you verified]
...
ALL ISSUES ADDRESSED: YES/NO
If any issue is not fixed, go back to Phase 2.
# Add fixed files
git add [fixed-files]
# Commit with descriptive message
git commit -m "fix: Address QA issues
Fixes:
- [Issue 1 title]
- [Issue 2 title]
Verified:
- All tests pass
- Issues verified locally"
=== QA FIXES COMPLETE ===
Issues fixed: [N]
1. [Issue 1] - FIXED
Commit: [hash]
2. [Issue 2] - FIXED
Commit: [hash]
All tests passing.
Ready for QA re-validation.
The QA → Fix → QA loop continues until:
Maximum iterations: 5
If max iterations reached without approval:
CRITICAL - Blocks sign-off:
MAJOR - Should fix:
MINOR - Nice to fix:
Before approving:
Why it's wrong: Shipping bugs to users.
Do this instead: Check EVERYTHING in the acceptance criteria.
Why it's wrong: Developer can't fix what they don't understand.
Do this instead: Exact file paths, line numbers, reproducible steps.
Why it's wrong: Introducing new bugs while fixing old ones.
Do this instead: Minimal changes. Fix only what QA found.
This skill works well with:
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
日本語の概要は準備中です。原文の説明を表示しています。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
日本語の概要は準備中です。原文の説明を表示しています。