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affaan-m/benchmark

affaan-m

benchmark

Measure performance baselines and detect regressions across browser Core Web Vitals (LCP, INP, CLS, page weight), API endpoint latency percentiles, and build/test feedback times, with before/after comparison stored in git-tracked .ecc/benchmarks JSON. Use when checking page speed, responding to 'it feels slow' reports, verifying launch performance targets, or comparing stack alternatives.

NewUpdated Sep 27, 2026

Benchmark — Performance Baseline & Regression Detection

When to Use

  • Before and after a PR to measure performance impact
  • Setting up performance baselines for a project
  • When users report "it feels slow"
  • Before a launch — ensure you meet performance targets
  • Comparing your stack against alternatives

How It Works

Mode 1: Page Performance

Measures real browser metrics via browser MCP:

1. Navigate to each target URL
2. Measure Core Web Vitals:
   - LCP (Largest Contentful Paint) — target < 2.5s
   - CLS (Cumulative Layout Shift) — target < 0.1
   - INP (Interaction to Next Paint) — target < 200ms
   - FCP (First Contentful Paint) — target < 1.8s
   - TTFB (Time to First Byte) — target < 800ms
3. Measure resource sizes:
   - Total page weight (target < 1MB)
   - JS bundle size (target < 200KB gzipped)
   - CSS size
   - Image weight
   - Third-party script weight
4. Count network requests
5. Check for render-blocking resources

Mode 2: API Performance

Benchmarks API endpoints:

1. Hit each endpoint 100 times
2. Measure: p50, p95, p99 latency
3. Track: response size, status codes
4. Test under load: 10 concurrent requests
5. Compare against SLA targets

Mode 3: Build Performance

Measures development feedback loop:

1. Cold build time
2. Hot reload time (HMR)
3. Test suite duration
4. TypeScript check time
5. Lint time
6. Docker build time

Mode 4: Before/After Comparison

Run before and after a change to measure impact:

/benchmark baseline    # saves current metrics
# ... make changes ...
/benchmark compare     # compares against baseline

Output:

| Metric | Before | After | Delta | Verdict |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | WARNING: WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ BETTER |
| Build | 12s | 14s | +2s | WARNING: WARN |

Output

Stores baselines in .ecc/benchmarks/ as JSON. Git-tracked so the team shares baselines.

Integration

  • CI: run /benchmark compare on every PR
  • Pair with /canary-watch for post-deploy monitoring
  • Pair with /browser-qa for full pre-ship checklist
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Overall Score

78/100

Grade

B

Good

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

82

Quality

74

Clarity

85

Completeness

68

Summary

The benchmark skill provides structured guidance for measuring performance baselines and detecting regressions across four domains: page performance (Core Web Vitals), API endpoint latency, build/test feedback times, and before/after comparison stored in git-tracked JSON. It integrates with browser and CI/CD workflows to enable developers to monitor performance impact of changes.

Detected Capabilities

file write (.ecc/benchmarks JSON)browser navigation and measurement (via browser MCP)API endpoint load testingbuild command executiongit workflow integrationcomparison and metric analysis

Trigger Keywords

Phrases that agents use to match this skill to user intent.

measure page speeddetect performance regressioncore web vitalsbenchmark api latencycompare before aftertrack build performanceperformance baselinelaunch readiness check

Use Cases

  • Measure Core Web Vitals impact before and after a PR
  • Set up performance baselines and track regressions over time
  • Investigate user reports of slow page performance
  • Verify launch performance targets are met
  • Compare performance across technology stack alternatives
  • Monitor API endpoint latency under load (p50, p95, p99)
  • Track build and test feedback loop performance (HMR, TypeScript checks, linting)
  • Generate before/after performance comparison reports with verdict indicators

Quality Notes

  • Skill clearly documents four distinct performance measurement modes with specific metric targets and acceptable ranges
  • Provides concrete use cases matching user intent (slow reports, launches, PR reviews, stack comparison)
  • Integration section suggests complementary workflows with canary-watch and browser-qa
  • Output format example is concrete and shows clear before/after comparison with verdict indicators
  • Scope is well-bounded: operates on developer's own project and URLs, stores results locally in .ecc/ directory
  • Measurement targets (e.g., LCP < 2.5s, Bundle < 200KB) provide clear success criteria
  • Before/after workflow is clearly explained with baseline save and compare commands
  • No error handling guidance documented for network failures, browser crashes, or timeout scenarios
  • Does not document how to handle missing baseline on first run or baseline invalidation
  • Does not specify which browser engines are used for page performance measurement or concurrency model for API load test
  • No guidance on metric confidence intervals or statistical significance of deltas
  • Missing details on how performance metrics are normalized across different environments or network conditions
Model: claude-haiku-4-5-20251001Analyzed: Sep 27, 2026

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Version History

  1. v2.0

    Contract changed: description

    ✦ AIExpands skill activation scope: adds Core Web Vitals, API latency, and build/test metrics to performance measurement; broadens use-case guidance.

    triggering2026-09-27

    LATEST
  2. v1.3

    Content updated

    ✦ AIAdds MIT license declaration.

    license2026-09-09

    View This Version
  3. v1.2

    Content updated

    ✦ AINo behavioral changes detected.

    2026-07-14

    View This Version
  4. v1.1

    Content updated

    ✦ AIAdds LICENSE file.

    2026-04-20

    View This Version
  5. v1.0

    2026-04-12

    View This VersionInitial version

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