quality-metrics — community quality-metrics, ai-news-influencer, community, ide skills

v1.0.0

このスキルについて

高度な品質モニタリングとDORAメトリクス分析を必要とするAIエージェントに最適です。 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

高度な品質モニタリングとDORAメトリクス分析を必要とするAIエージェントに最適です。 Measure quality effectively with actionable metrics. Use when establishing quality dashboards, defining KPIs, or evaluating test effectiveness.

このスキルを使用する理由

エージェントはデプロイ頻度、リードタイム、変更故障率などの重要なパフォーマンス指標を追跡し、エンゲージメント指標を利用して戦略を洗練し、品質ゲートと閾値を通じてコード品質を向上させることができます。

おすすめ

高度な品質モニタリングとDORAメトリクス分析を必要とするAIエージェントに最適です。

実現可能なユースケース for quality-metrics

開発の参考となる関連ニュースやトレンドを入手するためのTwitterのモニタリング
品質メトリクスとDORAの原則に基づいてLinkedInコンテンツを生成する
プロセス改善のためのバグエスケープレートと平均検出時間(MTTD)の分析

! セキュリティと制限

  • ソーシャルメディアのモニタリングとコンテンツ生成のためにTwitterとLinkedIn APIへのアクセスが必要
  • DORAメトリクスのみに焦点を当てているため、すべての開発環境やチームに適用できない可能性がある

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

⚡️ Ready to unleash?

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?

高度な品質モニタリングとDORAメトリクス分析を必要とするAIエージェントに最適です。 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/quality-metrics. 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: 開発の参考となる関連ニュースやトレンドを入手するためのTwitterのモニタリング, 品質メトリクスとDORAの原則に基づいてLinkedInコンテンツを生成する, プロセス改善のためのバグエスケープレートと平均検出時間(MTTD)の分析.

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?

ソーシャルメディアのモニタリングとコンテンツ生成のためにTwitterとLinkedIn APIへのアクセスが必要. DORAメトリクスのみに焦点を当てているため、すべての開発環境やチームに適用できない可能性がある.

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/quality-metrics. 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. Review the use cases, limitations, and setup path before rollout.

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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