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Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use this when writing or modifying Nix package definitions and related repo exports in this NUR repository.
Run nix-update across all packages in this repo, fix build failures, and land each package's changes as its own separate jj commit. Use when asked to "update all packages", "bump everything", "run nix-update on all packages", or similar bulk-update requests.
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
| name | benchmark |
| description | Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives. |
| metadata | {"origin":"ECC"} |
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
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
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
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 |
Stores baselines in .ecc/benchmarks/ as JSON. Git-tracked so the team shares baselines.
/benchmark compare on every PR/canary-watch for post-deploy monitoring/browser-qa for full pre-ship checklist