quality-metrics
关于
The quality-metrics skill helps developers establish actionable quality dashboards and KPIs by focusing on outcome-based measurements like DORA metrics. It guides users to avoid vanity metrics, set effective quality gates, and track trends over time. This optimized skill is ideal for evaluating test effectiveness and defining key performance indicators.
快速安装
Claude Code
推荐/plugin add https://github.com/proffesor-for-testing/agentic-qegit clone https://github.com/proffesor-for-testing/agentic-qe.git ~/.claude/skills/quality-metrics在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Quality Metrics
<default_to_action> When measuring quality or building dashboards:
- MEASURE outcomes (bug escape rate, MTTD) not activities (test count)
- FOCUS on DORA metrics: Deployment frequency, Lead time, MTTD, MTTR, Change failure rate
- AVOID vanity metrics: 100% coverage means nothing if tests don't catch bugs
- SET thresholds that drive behavior (quality gates block bad code)
- TREND over time: Direction matters more than absolute numbers
Quick Metric Selection:
- Speed: Deployment frequency, lead time for changes
- Stability: Change failure rate, MTTR
- Quality: Bug escape rate, defect density, test effectiveness
- Process: Code review time, flaky test rate
Critical Success Factors:
- Metrics without action are theater
- What you measure is what you optimize
- Trends matter more than snapshots </default_to_action>
Quick Reference Card
When to Use
- Building quality dashboards
- Defining quality gates
- Evaluating testing effectiveness
- Justifying quality investments
Meaningful vs Vanity Metrics
| ✅ 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 |
DORA Metrics
| 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 |
Quality Gate Thresholds
| Metric | Blocking Threshold | Warning |
|---|---|---|
| Test pass rate | 100% | - |
| Critical coverage | > 80% | > 70% |
| Security critical | 0 | - |
| Performance p95 | < 200ms | < 500ms |
| Flaky tests | < 2% | < 5% |
Core Metrics
Bug Escape Rate
Bug Escape Rate = (Production Bugs / Total Bugs Found) × 100
Target: < 10% (90% caught before production)
Test Effectiveness
Test Effectiveness = (Bugs Found by Tests / Total Bugs) × 100
Target: > 70%
Defect Density
Defect Density = Defects / KLOC
Good: < 1 defect per KLOC
Mean Time to Detect (MTTD)
MTTD = Time(Bug Reported) - Time(Bug Introduced)
Target: < 1 day for critical, < 1 week for others
Dashboard Design
// 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");
Quality Gate Configuration
{
"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 }
}
}
}
Agent-Assisted Metrics
// 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");
Agent Coordination Hints
Memory Namespace
aqe/quality-metrics/
├── dashboards/* - Dashboard configurations
├── trends/* - Historical metric data
├── gates/* - Gate evaluation results
└── alerts/* - Triggered alerts
Fleet Coordination
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'
});
Common Traps
| 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 |
Related Skills
- agentic-quality-engineering - Agent coordination
- cicd-pipeline-qe-orchestrator - Quality gates
- risk-based-testing - Risk-informed metrics
- shift-right-testing - Production metrics
Remember
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.
GitHub 仓库
相关推荐技能
Verification & Quality Assurance
其他该Skill为开发者提供自动化代码质量验证和可靠性保障系统,通过实时真值评分(0-1分制)和代码质量检查确保输出质量。当评分低于0.95阈值时会自动回滚问题代码,有效维护代码库稳定性。特别适合集成至CI/CD流程,用于监控Agent输出质量和保障项目可靠性。
performance-analysis
其他该Skill为Claude Flow群组提供全面的性能分析,能自动检测通信、处理和网络等瓶颈。它通过实时监控和性能剖析生成详细报告,并给出AI驱动的优化建议。开发者可快速识别系统性能问题并获得具体改进方案。
test-reporting-analytics
其他该Skill为开发者提供高级测试报告和质量分析功能,包括质量仪表板、趋势分析和预测性指标。它适用于沟通质量状态、跟踪测试趋势或基于数据做出决策的场景。关键特性支持自动化报告生成、执行级汇报,并能突出可操作的改进点。
performance-analysis
其他这个Claude Skill为开发者提供全面的性能分析和瓶颈检测功能,帮助优化Claude Flow群集的运行效率。它能实时监控系统性能、识别通信和处理瓶颈,并生成详细的优化建议报告。开发者可以用它快速定位性能问题并获得AI驱动的改进方案。
