cc-pagespeed
Skill0 starsChanged 27 days agoMITView on GitHub
Run the PageSpeed performance audit — 80/20 analysis of Core Web Vitals using the PageSpeed Insights API.
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- .agents/AGENTS.mdAGENTS.md
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- .claude/CLAUDE.mdCLAUDE.md
- CLAUDE.mdCLAUDE.md
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
Read PAGESPEED_API_KEY from the environment.
- Distinctive: line 28
Call the PageSpeed Insights API for the URL provided above.
- Distinctive: line 31
Call PSI directly via WebFetch and fetch the HTML source for manual resource-hint analysis.
- Very specific: line 67
Write the full performance report to {YYYY-MM-DD}_pagespeed-{hostname}-claude.md in the current directory.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
PageSpeed Performance Audit
Audit URL: $ARGUMENTS
Step 1 — Pre-flight (pagespeed-perf skill)
Invoke pagespeed-perf skill.
Read PAGESPEED_API_KEY from the environment. Compute the output filename:
{YYYY-MM-DD}_pagespeed-{hostname}-claude.md.
Log whether the API key was found. Proceed regardless — lab data is available without a key, but CrUX field data requires it.
Step 2 — Collect
Call the PageSpeed Insights API for the URL provided above.
- Strategy:
both(mobile + desktop) unless the user specified otherwise. - Call PSI directly via WebFetch and fetch the HTML source for manual resource-hint analysis.
Step 3 — Analyze
Extract from the PSI response:
- Core Web Vitals: LCP, INP, CLS, FCP, TBT, TTFB
- LCP element (type, selector, HTML snippet)
- Third-party scripts ordered by blocking time
- Render-blocking resources
- Unused JavaScript and CSS (savings in bytes and ms)
From the HTML source:
- Resource hints (
<link rel="preload|preconnect|prefetch">) - Images: lazy loading,
fetchpriority, format, dimensions - Fonts:
font-display, preloaded subsets - Blocking scripts in
<head>(noasync/defer)
Step 4 — Prioritize
Score each identified optimization using the 80/20 matrix:
Score = Impact × Ease (each 1–5, max 25).
Order findings by descending score. Mark optimizations with Score ≥ 20 as Critical 20%.
Step 5 — Report
Write the full performance report to
{YYYY-MM-DD}_pagespeed-{hostname}-claude.md in the current directory.
Required sections:
- Resumen Ejecutivo (2–3 paragraphs)
- Métricas Actuales (lab vs field table)
- Recursos con Mayor Impacto
- Plan de Acción Priorizado (80/20 table)
- Solución Técnica Detallada (per finding: problem, evidence, code, expected gain)
- Quick Wins / High Impact Changes / Roadmap
End every report with:
---
*Informe generado el {YYYY-MM-DD} · Herramienta: Claude Code + pagespeed-perf skill*
*API Key usada: {Sí / No} · Datos CrUX: {Disponibles / No disponibles}*Requirements
PAGESPEED_API_KEY — Optional but strongly recommended.
- With key: real CrUX field data + 25,000 requests/day quota.
- Without key: lab data only (Lighthouse); shared rate limit applies.
Set in your shell:
$env:PAGESPEED_API_KEY = "your-api-key" # Windows PowerShell
export PAGESPEED_API_KEY="your-api-key" # macOS / LinuxGet a free key: https://developers.google.com/speed/docs/insights/v5/get-started
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