Clawfu-skills 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

install

source · Clone the upstream repo

git clone https://github.com/guia-matthieu/clawfu-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/guia-matthieu/clawfu-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/seo-tools/lighthouse-audit" ~/.claude/skills/guia-matthieu-clawfu-skills-lighthouse-audit && rm -rf "$T"

manifest: skills/seo-tools/lighthouse-audit/SKILL.md

source content

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 - Fix structured data issues
image-batch - Optimize images for LCP
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