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.
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Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When measuring quality or building dashboards:
Quick Metric Selection:
Critical Success Factors:
| ✅ Meaningful | ❌ Vanity |
|---|---|
| Bug escape rate | Test case count |
| MTTD (detection) | Lines of test code |
| MTTR (recovery) | Test executions |
| Change failure rate | Coverage % (alone) |
| Lead time for changes | Requirements traced |
| Metric | Elite | High | Medium | Low |
|---|---|---|---|---|
| Deploy Frequency | On-demand | Weekly | Monthly | Yearly |
| Lead Time | < 1 hour | < 1 week | < 1 month | > 6 months |
| Change Failure Rate | < 5% | < 15% | < 30% | > 45% |
| MTTR | < 1 hour | < 1 day | < 1 week | > 1 month |
| Metric | Blocking Threshold | Warning |
|---|---|---|
| Test pass rate | 100% | - |
| Critical coverage | > 80% | > 70% |
| Security critical | 0 | - |
| Performance p95 | < 200ms | < 500ms |
| Flaky tests | < 2% | < 5% |
Bug Escape Rate = (Production Bugs / Total Bugs Found) × 100
Target: < 10% (90% caught before production)
Test Effectiveness = (Bugs Found by Tests / Total Bugs) × 100
Target: > 70%
Defect Density = Defects / KLOC
Good: < 1 defect per KLOC
MTTD = Time(Bug Reported) - Time(Bug Introduced)
Target: < 1 day for critical, < 1 week for others
// Agent generates quality dashboard
await Task("Generate Dashboard", {
metrics: {
delivery: ['deployment-frequency', 'lead-time', 'change-failure-rate'],
quality: ['bug-escape-rate', 'test-effectiveness', 'defect-density'],
stability: ['mttd', 'mttr', 'availability'],
process: ['code-review-time', 'flaky-test-rate', 'coverage-trend']
},
visualization: 'grafana',
alerts: {
critical: { bug_escape_rate: '>20%', mttr: '>24h' },
warning: { coverage: '<70%', flaky_rate: '>5%' }
}
}, "qe-quality-analyzer");
{
"qualityGates": {
"commit": {
"coverage": { "min": 80, "blocking": true },
"lint": { "errors": 0, "blocking": true }
},
"pr": {
"tests": { "pass": "100%", "blocking": true },
"security": { "critical": 0, "blocking": true },
"coverage_delta": { "min": 0, "blocking": false }
},
"release": {
"e2e": { "pass": "100%", "blocking": true },
"performance_p95": { "max_ms": 200, "blocking": true },
"bug_escape_rate": { "max": "10%", "blocking": false }
}
}
}
// Calculate quality trends
await Task("Quality Trend Analysis", {
timeframe: '90d',
metrics: ['bug-escape-rate', 'mttd', 'test-effectiveness'],
compare: 'previous-90d',
predictNext: '30d'
}, "qe-quality-analyzer");
// Evaluate quality gate
await Task("Quality Gate Evaluation", {
buildId: 'build-123',
environment: 'staging',
metrics: currentMetrics,
policy: qualityPolicy
}, "qe-quality-gate");
aqe/quality-metrics/
├── dashboards/* - Dashboard configurations
├── trends/* - Historical metric data
├── gates/* - Gate evaluation results
└── alerts/* - Triggered alerts
const metricsFleet = await FleetManager.coordinate({
strategy: 'quality-metrics',
agents: [
'qe-quality-analyzer', // Trend analysis
'qe-test-executor', // Test metrics
'qe-coverage-analyzer', // Coverage data
'qe-production-intelligence', // Production metrics
'qe-quality-gate' // Gate decisions
],
topology: 'mesh'
});
| Trap | Problem | Solution |
|---|---|---|
| Coverage worship | 100% coverage, bugs still escape | Measure bug escape rate instead |
| Test count focus | Many tests, slow feedback | Measure execution time |
| Activity metrics | Busy work, no outcomes | Measure outcomes (MTTD, MTTR) |
| Point-in-time | Snapshot without context | Track trends over time |
Measure outcomes, not activities. Bug escape rate > test count. MTTD/MTTR > coverage %. Trends > snapshots. Set gates that block bad code. What you measure is what you optimize.
With Agents: Agents track metrics automatically, analyze trends, trigger alerts, and make gate decisions. Use agents to maintain continuous quality visibility.
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
中文简介正在准备中。当前显示原文说明。