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lighthouse-audit

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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Atualizado há 7 meses

Ver no GitHub ↗ Licença: MIT

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/plugin marketplace add guia-matthieu/clawfu-skills

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Lighthouse Audit

Automate Google Lighthouse audits to measure and track Core Web Vitals, SEO, and accessibility - the same metrics Google uses for search ranking.

When to Use This Skill

  • Performance optimization - Measure LCP, FID, CLS before and after changes
  • SEO audits - Check technical SEO issues (meta tags, structured data, etc.)
  • Accessibility checks - Identify a11y issues for compliance
  • Client reporting - Generate professional performance reports
  • Monitoring - Track scores over time across multiple pages

What Claude Does vs What You Decide

| 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 |

Dependencies

pip install click pandas jinja2
# Also requires Chrome and Lighthouse CLI
# npm install -g lighthouse
# Or use Chrome DevTools built-in Lighthouse

Commands

Single URL Audit

python scripts/main.py audit https://example.com --categories performance,seo
python scripts/main.py audit https://example.com --format html --output report.html

Batch Audit

python scripts/main.py batch urls.txt --output results/
python scripts/main.py batch urls.txt --categories performance --format csv

Compare Before/After

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

Monitor Over Time

python scripts/main.py history https://example.com --days 30
python scripts/main.py history https://example.com --plot

Examples

Example 1: Full Site Performance Audit

# 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

Example 2: Before/After Comparison

# 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

Example 3: Generate Client Report

# 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

Audit Categories

| 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 |

Core Web Vitals Thresholds

| 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 |

Output Formats

| Format | Use Case | Content | |--------|----------|---------| | json | Automation, storage | Full raw data | | csv | Spreadsheets, analysis | Summary scores | | html | Client reports | Visual report | | md | Documentation | Markdown summary |

Skill Boundaries

What This Skill Does Well

  • Structuring data analysis
  • Identifying patterns and trends
  • Creating visualization frameworks
  • Calculating statistical measures

What This Skill Cannot Do

  • Access your actual data
  • Replace statistical expertise
  • Make business decisions
  • Guarantee prediction accuracy

Related Skills

  • [schema-markup](../schema-markup/) - Fix structured data issues
  • [image-batch](../../automation/image-batch/) - Optimize images for LCP
  • [link-checker](../link-checker/) - Find broken links

Skill Metadata

  • Mode: centaur
category: seo-tools
subcategory: performance
dependencies: [lighthouse, click, pandas]
difficulty: beginner
time_saved: 3+ hours/week
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