Benchmark — Performance Baseline & Regression Detection
SkillAI & modelsUse this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Benchmark — Performance Baseline & Regression Detection skill
What this skill tells your AI
The instructions your AI receives, as published by mturac/everything-openai-codex in skills/benchmark/SKILL.md and read by ahel’s review.
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 compareon every PR - Pair with
/canary-watchfor post-deploy monitoring - Pair with
/browser-qafor full pre-ship checklist
Signals
- GitHub stars
- 90
- Forks
- 2
- Last commit
- Aug 2026
- Hacker News mentions
- 20
ahel recommends instead
Advanced
- Catalog kind
- skill
- Gateway key
benchmark-mturac- Source
- github.com/mturac/everything-openai-codex