/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
$ npx -y skills add loulanyue/awesome-claude-notes --skill benchmark --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/benchmark
Context preview
The summary Claude sees to decide when to auto-load this skill.
- 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
SKILL.md
benchmark.SKILL.mdBenchmark — 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
Read more
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
Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.
Repo: loulanyue/awesome-claude-notes
Other skills on awesome-claude-notes.
- /agent-eval
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Open skill - /agent-harness-construction
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Open skill - /agentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
Open skill - /ai-first-engineering
Engineering operating model for teams where AI agents generate a large share of implementation output.
Open skill - /ai-regression-testing
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
Open skill - /android-clean-architecture
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
Open skill

