Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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。
Conduct context-driven code reviews focusing on quality, testability, and maintainability. Use when reviewing code, providing feedback, or establishing review practices.
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。
実行スコア 未検証
インストール方法を見る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。
<default_to_action> When reviewing code or establishing review practices:
Quick Review Checklist:
Critical Success Factors:
| Level | Icon | Meaning | Action |
|---|---|---|---|
| Blocker | 🔴 | Bug/security/crash | Must fix before merge |
| Major | 🟡 | Logic issue/test gap | Should fix before merge |
| Minor | 🟢 | Style/naming | Nice to fix |
| Suggestion | 💡 | Alternative approach | Consider for future |
| Lines Changed | Recommendation |
|---|---|
| < 200 | Single review session |
| 200-400 | Review in chunks |
| > 400 | Request PR split |
| ✅ Review | ❌ Skip |
|---|---|
| Logic correctness | Formatting (use linter) |
| Security risks | Naming preferences |
| Test coverage | Architecture debates |
| Performance issues | Style opinions |
| Error handling | Trivial changes |
🔴 **BLOCKER: SQL Injection Risk**
This query is vulnerable to SQL injection:
```javascript
db.query(`SELECT * FROM users WHERE id = ${userId}`)
Fix: Use parameterized queries:
db.query('SELECT * FROM users WHERE id = ?', [userId])
Why: User input directly in SQL allows attackers to execute arbitrary queries.
### Major (Should Fix)
```markdown
🟡 **MAJOR: Missing Error Handling**
What happens if `fetchUser()` throws? The error bubbles up unhandled.
**Suggestion:** Add try/catch with appropriate error response:
```javascript
try {
const user = await fetchUser(id);
return user;
} catch (error) {
logger.error('Failed to fetch user', { id, error });
throw new NotFoundError('User not found');
}
### Minor (Nice to Fix)
```markdown
🟢 **minor:** Variable name could be clearer
`d` doesn't convey meaning. Consider `daysSinceLastLogin`.
💡 **suggestion:** Consider extracting this to a helper
This validation logic appears in 3 places. A `validateEmail()` helper would reduce duplication. Not blocking, but might be worth a follow-up PR.
Reviews must meet a minimum weighted finding score of 3.0 (CRITICAL=3, HIGH=2, MEDIUM=1, LOW=0.5, INFORMATIONAL=0.25). If the initial review falls short, run the qe-devils-advocate agent as a meta-reviewer to find additional observations. Every review should have at least 3 actionable observations.
// Comprehensive code review
await Task("Code Review", {
prNumber: 123,
checks: ['security', 'performance', 'testability', 'maintainability'],
feedbackLevels: ['blocker', 'major', 'minor'],
autoApprove: { maxBlockers: 0, maxMajor: 2 }
}, "qe-quality-analyzer");
// Security-focused review
await Task("Security Review", {
prFiles: changedFiles,
scanTypes: ['injection', 'auth', 'secrets', 'dependencies']
}, "qe-security-scanner");
// Test coverage review
await Task("Coverage Review", {
prNumber: 123,
requireNewTests: true,
minCoverageDelta: 0
}, "qe-coverage-analyzer");
aqe/code-review/
├── review-history/* - Past review decisions
├── patterns/* - Common issues by team/repo
├── feedback-templates/* - Reusable feedback
└── metrics/* - Review turnaround time
const reviewFleet = await FleetManager.coordinate({
strategy: 'code-review',
agents: [
'qe-quality-analyzer', // Logic, maintainability
'qe-security-scanner', // Security risks
'qe-performance-tester', // Performance issues
'qe-coverage-analyzer' // Test coverage
],
topology: 'parallel'
});
| ✅ Do | ❌ Don't |
|---|---|
| "Have you considered...?" | "This is wrong" |
| Explain why it matters | Just say "fix this" |
| Acknowledge good code | Only point out negatives |
| Suggest, don't demand | Be condescending |
| Review < 400 lines | Review 2000 lines at once |
Prioritize feedback: 🔴 Blocker → 🟡 Major → 🟢 Minor → 💡 Suggestion. Focus on bugs and security, not style. Ask questions, don't command. Review < 400 lines at a time. Fast feedback (< 24h) beats thorough feedback.
With Agents: Agents automate security, performance, and coverage checks, freeing human reviewers to focus on logic and design. Use agents for consistent, fast initial review.
/security-testing for security-focused review/qe-coverage-analysis on changed files/qe-quality-assessment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。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。