What is quality-metrics?

Ideal for AI Agents needing advanced quality monitoring and DORA metrics analysis for optimized software development. 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: Monitoring Twitter for relevant news and trends to inform development, Generating engaging LinkedIn content based on quality metrics and DORA principles, Analyzing bug escape rates and mean time to detection (MTTD) for process improvement.

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?

Requires access to Twitter and LinkedIn APIs for social media monitoring and content generation. Focuses on DORA metrics, which might not be applicable to all development environments or teams.

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

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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

Name: quality-metrics
Availability: InStock
Author: natea

<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

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

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

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.

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

About this Skill

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