How do I install the Benchmark skill?

Run npx skillmds@latest add jantoniofc/benchmark in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Benchmark safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Benchmark?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Benchmark free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Benchmark?

JantonioFC (@jantoniofc) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Benchmark by jantoniofc — Agent Skill

Name: Benchmark
Rating: 4.2 (5 reviews)
Author: JantonioFC

Benchmark

- 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

JantonioFC Updated Sep 8, 2026 6 repo stars

File contents

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 | ⚠ WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ BETTER |
| Build | 12s | 14s | +2s | ⚠ 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

---name: benchmarkdescription: - 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 alternativeslicense: MIT---# 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 PerformanceMeasures real browser metrics via browser MCP:```1. Navigate to each target URL2. 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 < 800ms3. Measure resource sizes: - Total page weight (target < 1MB) - JS bundle size (target < 200KB gzipped) - CSS size - Image weight - Third-party script weight4. Count network requests5. Check for render-blocking resources```### Mode 2: API PerformanceBenchmarks API endpoints:```1. Hit each endpoint 100 times2. Measure: p50, p95, p99 latency3. Track: response size, status codes4. Test under load: 10 concurrent requests5. Compare against SLA targets```### Mode 3: Build PerformanceMeasures development feedback loop:```1. Cold build time2. Hot reload time (HMR)3. Test suite duration4. TypeScript check time5. Lint time6. Docker build time```### Mode 4: Before/After ComparisonRun 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 | ⚠ WARN || Bundle | 180KB | 175KB | -5KB | ✓ BETTER || Build | 12s | 14s | +2s | ⚠ WARN |```## OutputStores 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

Frequently asked questions