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trash-panda-v91-beta

quality-engineering

by trash-panda-v91-beta

A modular and declarative dotfiles configuration using Nix Flakes, Home Manager, and nix-darwin.

1🍴 0📅 Jan 22, 2026

SKILL.md


name: quality-engineering description: Use when designing test strategies, implementing test automation, or establishing QA processes

Quality Engineering

Guidelines for QA processes, test automation, and performance engineering.

When to Use

  • Designing test strategies for new features
  • Setting up or improving test automation
  • Performance testing and optimization
  • Capacity planning and load testing
  • Establishing quality metrics

Quality Philosophy

  • Prevention over detection - Engage early to prevent defects
  • Test behavior, not implementation - Focus on observable outcomes
  • No failing builds - Never merge broken code
  • Continuous improvement - Regularly refine processes

Test Strategy

Test Pyramid

       /\
      /E2E\      ← Few, critical paths
     /------\
    /  Integ  \   ← Moderate, key integrations
   /------------\
  /    Unit      \ ← Many, fast, isolated

Coverage Targets

  • Unit tests: > 80% line coverage
  • Integration: Key API paths covered
  • E2E: Critical user journeys

Test Design Patterns

Arrange-Act-Assert (AAA)

// Arrange: Setup preconditions
const user = createTestUser();

// Act: Execute behavior
const result = await login(user);

// Assert: Verify outcome
expect(result.success).toBe(true);

Test Characteristics

  • Isolated: No shared state between tests
  • Deterministic: Same result every run
  • Fast: Quick feedback loop
  • Readable: Self-documenting names

Definition of Done

Feature is complete when:

  • All tests passing (unit, integration, E2E)
  • Code meets style guides
  • No console errors or unhandled exceptions
  • API changes documented
  • Performance budgets met

Performance Engineering

Systematic Approach

  1. Baseline: Measure before optimizing
  2. Identify: Profile to find bottlenecks
  3. Budget: Set clear SLOs
  4. Optimize: Implement improvements
  5. Validate: Measure impact
  6. Monitor: Continuous production tracking

Key Metrics

LayerMetrics
FrontendLCP, INP, CLS, TTFB
APIResponse time, throughput, error rate
DatabaseQuery time, connections, locks
InfrastructureCPU, memory, I/O, network

Performance Checklist

  • Established performance baselines
  • Load testing simulates realistic traffic
  • Database queries optimized
  • Caching strategy implemented
  • CDN configured for static assets
  • Monitoring dashboards in place
  • Alerting on SLO breaches

Test Automation

Framework Selection

TypeRecommended Tools
UnitJest, Pytest, JUnit
IntegrationTestcontainers, SuperTest
E2EPlaywright, Cypress
Loadk6, Locust, Gatling
CoverageIstanbul, JaCoCo

CI/CD Integration

# Pipeline stages
stages:
  - lint      # Fast feedback
  - unit      # Parallel execution
  - build     # Artifact creation
  - integration  # Service testing
  - e2e       # Critical paths
  - performance  # Load testing (optional)

Test Data Management

  • Use factories/fixtures for consistent data
  • Isolate test data from production
  • Clean up after tests
  • Consider data masking for sensitive info

Quality Metrics

MetricTargetPurpose
Test Coverage> 80%Code confidence
Test Pass Rate> 98%Stability
Flaky Test Rate< 2%Reliability
Build Time< 10 minFast feedback
MTTR< 1 hourRecovery speed

Deliverables

  • Test Strategy Document: Scope, objectives, methodology
  • Test Cases: Step-by-step with expected results
  • Automated Test Suite: Maintainable, organized tests
  • CI Pipeline Config: Automated quality gates
  • Coverage Reports: Visibility into tested code
  • Performance Dashboards: Real-time metrics
  • Bug Reports: Clear reproduction steps, severity

Anti-Patterns to Avoid

  • Testing implementation details instead of behavior
  • Flaky tests that pass/fail randomly
  • Slow test suites blocking development
  • Missing edge case coverage
  • Manual-only regression testing
  • No performance testing until production issues

Score

Total Score

55/100

Based on repository quality metrics

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