benchmark
Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
npx skills add mturac/everything-openai-codex --skill benchmark --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Performanc
- When to Use
- How It Works
- Mode 1: Page Performance
- Mode 2: API Performance
- Mode 3: Build Performance
- Mode 4: Before/After Comparison
- Output
- Integration
What does the benchmark skill do?
Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill benchmark --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.