Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Run automated Lighthouse audits for Core Web Vitals and SEO. Use when: checking page performance; auditing SEO technical issues; monitoring Core Web Vitals; comparing before/after optimization; batch auditing multiple URLs
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Automate Google Lighthouse audits to measure and track Core Web Vitals, SEO, and accessibility - the same metrics Google uses for search ranking.
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
pip install click pandas jinja2
# Also requires Chrome and Lighthouse CLI
# npm install -g lighthouse
# Or use Chrome DevTools built-in Lighthouse
python scripts/main.py audit https://example.com --categories performance,seo
python scripts/main.py audit https://example.com --format html --output report.html
python scripts/main.py batch urls.txt --output results/
python scripts/main.py batch urls.txt --categories performance --format csv
python scripts/main.py compare https://example.com --baseline scores.json
python scripts/main.py compare https://example.com --baseline-url https://staging.example.com
python scripts/main.py history https://example.com --days 30
python scripts/main.py history https://example.com --plot
# Create URL list
cat > urls.txt << EOF
https://example.com/
https://example.com/pricing
https://example.com/features
https://example.com/blog
EOF
# Run batch audit
python scripts/main.py batch urls.txt --categories performance,seo,accessibility
# Output: results/audit_2024-01-15/
# ├── example.com_.json
# ├── example.com_pricing.json
# ├── example.com_features.json
# ├── example.com_blog.json
# └── summary.csv
# Save baseline
python scripts/main.py audit https://example.com --output baseline.json
# Make optimizations...
# Compare
python scripts/main.py compare https://example.com --baseline baseline.json
# Output:
# Core Web Vitals Comparison
# ─────────────────────────────
# Metric Before After Change
# LCP 3.2s 1.8s -44% ✓
# FID 120ms 45ms -63% ✓
# CLS 0.25 0.08 -68% ✓
# Performance 52 89 +37 pts
# Full audit with HTML report
python scripts/main.py audit https://client-site.com \
--format html \
--output client-report.html \
--include-screenshots
# Output: Professional HTML report with:
# - Executive summary
# - Core Web Vitals scores
# - Screenshots of issues
# - Prioritized recommendations
| Category | Checks | Impact |
|---|---|---|
performance | LCP, FID, CLS, TTFB, Speed Index | Search ranking |
seo | Meta tags, headings, links, mobile | Search visibility |
accessibility | WCAG compliance, contrast, labels | Compliance |
best-practices | HTTPS, security, modern APIs | Trust |
pwa | Service worker, manifest, offline | App-like experience |
| Metric | Good | Needs Improvement | Poor |
|---|---|---|---|
| LCP (Largest Contentful Paint) | ≤2.5s | 2.5s-4.0s | >4.0s |
| FID (First Input Delay) | ≤100ms | 100ms-300ms | >300ms |
| CLS (Cumulative Layout Shift) | ≤0.1 | 0.1-0.25 | >0.25 |
| INP (Interaction to Next Paint) | ≤200ms | 200ms-500ms | >500ms |
| Format | Use Case | Content |
|---|---|---|
json | Automation, storage | Full raw data |
csv | Spreadsheets, analysis | Summary scores |
html | Client reports | Visual report |
md | Documentation | Markdown summary |
category: seo-tools
subcategory: performance
dependencies: [lighthouse, click, pandas]
difficulty: beginner
time_saved: 3+ hours/week
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
中文简介正在准备中。当前显示原文说明。
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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