Benchmark

Skill loulanyue/awesome-claude-notes/skills/benchmark

Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.From the repository description

Install

npx -y skills add loulanyue/awesome-claude-notes --skill benchmark

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

runs commandsInstructs the agent to run 2 commands, including `/benchmark baseline` and 1 more.

SKILL.md

2.1 KB, 599 tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 2 of the 12 instructions most evals benchmarks skills give in 599 tokens

Counted across 499 of the 513 authors here whose files we hold, read 2026-09-06

Spawn with-skill and baseline runs in the same turnin 31 of 499, across 24 files
Keep SKILL.md under 500 linesin 31 of 499, across 24 files
Draft assertions while test runs are in progressin 31 of 499, across 24 files
Compare against the baseline after changeshere, and in 31 of 499, across 13 files
Define evals before codingin 26 of 499, across 17 files
Run evals frequently during developmentin 25 of 499, across 16 files
Keep evals fastin 24 of 499, across 15 files
Version evals with codein 24 of 499, across 15 files
Generate the eval viewer before evaluating outputs yourselfin 24 of 499, across 17 files
Generate an eval report after runsin 24 of 499, across 15 files
Track pass@k metrics over timein 22 of 499, across 14 files
Save a baseline before making changeshere, and in 21 of 499, across 9 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.