quality-metrics — community quality-metrics, ai-news-influencer, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0

이 스킬 정보

고급 품질 모니터링과 DORA 메트릭스 분석을 통해 최적화된 소프트웨어 개발이 필요한 AI 에이전트에게 적합합니다. Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.

natea natea
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Updated: 1/13/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
56
Canonical Locale
en
Detected Body Locale
en

고급 품질 모니터링과 DORA 메트릭스 분석을 통해 최적화된 소프트웨어 개발이 필요한 AI 에이전트에게 적합합니다. Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.

이 스킬을 사용하는 이유

에이전트가 배포 빈도, 리드 타임, 변경 실패율 등의 주요 성능 지표를 추적할 수 있으며, 참여 지표를 활용하여 전략을 개선하고 품질 문과閾値를 통해 코드 품질을 향상시킬 수 있습니다.

최적의 용도

고급 품질 모니터링과 DORA 메트릭스 분석을 통해 최적화된 소프트웨어 개발이 필요한 AI 에이전트에게 적합합니다.

실행 가능한 사용 사례 for quality-metrics

개발에 대한 정보를 제공하기 위한 관련 뉴스 및 트렌드를 위한 Twitter 모니터링
품질 메트릭스와 DORA 원칙을 기반으로 한 LinkedIn 콘텐츠 생성
프로세스 개선을 위한 버그 이탈률 및 평균 탐지 시간(MTTD) 분석

! 보안 및 제한 사항

  • 소셜 미디어 모니터링 및 콘텐츠 생성을 위해 Twitter 및 LinkedIn API에 대한 액세스가 필요합니다.
  • DORA 메트릭스만에 초점을 맞추고 있으므로 모든 개발 환경 또는 팀에 적용할 수 없는 경우가 있습니다.

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is quality-metrics?

고급 품질 모니터링과 DORA 메트릭스 분석을 통해 최적화된 소프트웨어 개발이 필요한 AI 에이전트에게 적합합니다. Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.

How do I install quality-metrics?

Run the command: npx killer-skills add natea/ai-news-influencer. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for quality-metrics?

Key use cases include: 개발에 대한 정보를 제공하기 위한 관련 뉴스 및 트렌드를 위한 Twitter 모니터링, 품질 메트릭스와 DORA 원칙을 기반으로 한 LinkedIn 콘텐츠 생성, 프로세스 개선을 위한 버그 이탈률 및 평균 탐지 시간(MTTD) 분석.

Which IDEs are compatible with quality-metrics?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for quality-metrics?

소셜 미디어 모니터링 및 콘텐츠 생성을 위해 Twitter 및 LinkedIn API에 대한 액세스가 필요합니다.. DORA 메트릭스만에 초점을 맞추고 있으므로 모든 개발 환경 또는 팀에 적용할 수 없는 경우가 있습니다..

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add natea/ai-news-influencer. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use quality-metrics immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

quality-metrics

Install quality-metrics, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Quality Metrics

<default_to_action> When measuring quality or building dashboards:

  1. MEASURE outcomes (bug escape rate, MTTD) not activities (test count)
  2. FOCUS on DORA metrics: Deployment frequency, Lead time, MTTD, MTTR, Change failure rate
  3. AVOID vanity metrics: 100% coverage means nothing if tests don't catch bugs
  4. SET thresholds that drive behavior (quality gates block bad code)
  5. 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 rateTest case count
MTTD (detection)Lines of test code
MTTR (recovery)Test executions
Change failure rateCoverage % (alone)
Lead time for changesRequirements traced

DORA Metrics

MetricEliteHighMediumLow
Deploy FrequencyOn-demandWeeklyMonthlyYearly
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

MetricBlocking ThresholdWarning
Test pass rate100%-
Critical coverage> 80%> 70%
Security critical0-
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

typescript
1// Agent generates quality dashboard 2await Task("Generate Dashboard", { 3 metrics: { 4 delivery: ['deployment-frequency', 'lead-time', 'change-failure-rate'], 5 quality: ['bug-escape-rate', 'test-effectiveness', 'defect-density'], 6 stability: ['mttd', 'mttr', 'availability'], 7 process: ['code-review-time', 'flaky-test-rate', 'coverage-trend'] 8 }, 9 visualization: 'grafana', 10 alerts: { 11 critical: { bug_escape_rate: '>20%', mttr: '>24h' }, 12 warning: { coverage: '<70%', flaky_rate: '>5%' } 13 } 14}, "qe-quality-analyzer");

Quality Gate Configuration

json
1{ 2 "qualityGates": { 3 "commit": { 4 "coverage": { "min": 80, "blocking": true }, 5 "lint": { "errors": 0, "blocking": true } 6 }, 7 "pr": { 8 "tests": { "pass": "100%", "blocking": true }, 9 "security": { "critical": 0, "blocking": true }, 10 "coverage_delta": { "min": 0, "blocking": false } 11 }, 12 "release": { 13 "e2e": { "pass": "100%", "blocking": true }, 14 "performance_p95": { "max_ms": 200, "blocking": true }, 15 "bug_escape_rate": { "max": "10%", "blocking": false } 16 } 17 } 18}

Agent-Assisted Metrics

typescript
1// Calculate quality trends 2await Task("Quality Trend Analysis", { 3 timeframe: '90d', 4 metrics: ['bug-escape-rate', 'mttd', 'test-effectiveness'], 5 compare: 'previous-90d', 6 predictNext: '30d' 7}, "qe-quality-analyzer"); 8 9// Evaluate quality gate 10await Task("Quality Gate Evaluation", { 11 buildId: 'build-123', 12 environment: 'staging', 13 metrics: currentMetrics, 14 policy: qualityPolicy 15}, "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

typescript
1const metricsFleet = await FleetManager.coordinate({ 2 strategy: 'quality-metrics', 3 agents: [ 4 'qe-quality-analyzer', // Trend analysis 5 'qe-test-executor', // Test metrics 6 'qe-coverage-analyzer', // Coverage data 7 'qe-production-intelligence', // Production metrics 8 'qe-quality-gate' // Gate decisions 9 ], 10 topology: 'mesh' 11});

Common Traps

TrapProblemSolution
Coverage worship100% coverage, bugs still escapeMeasure bug escape rate instead
Test count focusMany tests, slow feedbackMeasure execution time
Activity metricsBusy work, no outcomesMeasure outcomes (MTTD, MTTR)
Point-in-timeSnapshot without contextTrack trends over time


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.

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