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。
Advanced test reporting, quality dashboards, predictive analytics, trend analysis, and executive reporting for QE metrics. Use when communicating quality status, tracking trends, or making data-driven 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。
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 building test reports:
Dashboard Quick Setup:
+------------------+------------------+------------------+
| Tests Passed | Code Coverage | Flaky Tests |
| 1,247/1,250 ✅ | 82.3% ⬆️ +2.1% | 1.2% ⬇️ -0.3% |
+------------------+------------------+------------------+
| Critical Bugs | Deploy Freq | MTTR |
| 0 open ✅ | 12x/day ⬆️ | 2.3h ⬇️ |
+------------------+------------------+------------------+
Key Metrics by Audience:
| Category | Metric | Target |
|---|---|---|
| Execution | Pass Rate | >98% |
| Execution | Flaky Test % | <2% |
| Execution | Suite Duration | <10 min |
| Coverage | Line Coverage | >80% |
| Coverage | Branch Coverage | >70% |
| Quality | Escaped Defects | <5/release |
| Quality | MTTR | <4 hours |
| Efficiency | Automation Rate | >90% |
| Symbol | Meaning | Action |
|---|---|---|
| ⬆️ | Improving | Continue current approach |
| ⬇️ | Declining | Investigate root cause |
| ➡️ | Stable | Maintain or improve |
| ⚠️ | Threshold breach | Immediate attention |
Live quality status for CI/CD
- Build status (green/red)
- Test results (pass/fail counts)
- Coverage delta
- Flaky test alerts
## Sprint 47 Quality Summary
### Metrics
| Metric | Value | Trend |
|--------|-------|-------|
| Tests Added | +47 | ⬆️ |
| Coverage | 82.3% | ⬆️ +2.1% |
| Bugs Found | 12 | ➡️ |
| Escaped | 0 | ✅ |
### Highlights
- ✅ Zero escaped defects
- ⚠️ E2E suite now 45min (target: 30min)
### Actions
1. Optimize slow E2E tests
2. Add coverage for payment module
## Monthly Quality Report - Oct 2025
### Executive Summary
✅ Production uptime: 99.97% (target: 99.95%)
✅ Deploy frequency: 12x/day (up from 8x)
⚠️ Coverage: 82.3% (target: 85%)
### Business Impact
- Automation saves 120 hrs/month
- Bug cost: $150/bug found vs $5,000 escaped
- Estimated annual savings: $450K
### Recommendations
1. Invest in performance testing tooling
2. Hire senior QE for mobile coverage
// Predict test failures
const prediction = await Task("Predict Failures", {
codeChanges: prDiff,
historicalData: last90Days,
model: 'gradient-boosting'
}, "qe-quality-analyzer");
// Returns:
// {
// failureProbability: 0.73,
// likelyFailingTests: ['payment.test.ts'],
// suggestedAction: 'Review payment module carefully',
// confidence: 0.89
// }
// Trend analysis with anomaly detection
const trends = await Task("Analyze Trends", {
metrics: ['passRate', 'coverage', 'flakyRate'],
period: '30d',
detectAnomalies: true
}, "qe-quality-analyzer");
// Generate comprehensive quality report
const report = await Task("Generate Quality Report", {
period: 'sprint',
audience: 'executive',
includeROI: true,
includeTrends: true
}, "qe-quality-analyzer");
// Real-time quality gate check
const gateResult = await Task("Quality Gate Check", {
metrics: currentMetrics,
thresholds: qualityPolicy,
environment: 'production'
}, "qe-quality-gate");
aqe/reporting/
├── dashboards/* - Dashboard configurations
├── reports/* - Generated reports
├── trends/* - Trend analysis data
└── predictions/* - Predictive model outputs
const reportingFleet = await FleetManager.coordinate({
strategy: 'quality-reporting',
agents: [
'qe-quality-analyzer', // Metrics aggregation
'qe-quality-gate', // Threshold validation
'qe-deployment-readiness' // Release readiness
],
topology: 'parallel'
});
Measure to improve. Report to communicate.
Good reports:
Data without action is noise. Action without data is guessing.
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。