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Generate a professional PDF report from GEO audit data using ReportLab. Creates a polished, client-ready PDF with score gauges, bar charts, platform readiness visualizations, color-coded tables, and prioritized action plans.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
Monthly delta tracking and progress reporting for GEO clients. Compares two GEO audits (baseline vs. current), calculates score improvements across all categories, tracks action item completion, and generates a "here's your progress" client report. Use when user says "compare", "delta", "monthly report", "progress", "confronta", "progressi", "report mensile", or when running a monthly client check-in.
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure
Auto-generate a professional, client-ready GEO service proposal from audit data. Creates a full proposal in markdown and PDF including executive summary, findings, recommended service packages (Basic/Standard/Premium), pricing, timeline, and terms. Use when user says "proposal", "proposta", "offerta", "preventivo", "generate proposal", or after completing a GEO audit for a prospect.
| name | geo-report-pdf |
| description | Generate a professional PDF report from GEO audit data using ReportLab. Creates a polished, client-ready PDF with score gauges, bar charts, platform readiness visualizations, color-coded tables, and prioritized action plans. |
| version | 1.0.0 |
| author | geo-seo-claude |
| tags | ["geo","pdf","report","client-deliverable","professional"] |
| allowed-tools | Read, Grep, Glob, Bash, WebFetch, Write |
This skill generates a professional, visually polished PDF report from GEO audit data. The PDF includes score gauges, bar charts, platform readiness visualizations, color-coded tables, and a prioritized action plan — ready to deliver directly to clients.
Two PDF paths — pick one. For a branded, client-ready deliverable, prefer the TypeScript
tools/audit-report-kit(react-pdf, THL brand tokens, provenance tags,—for null scores, compile-checked JSON-LD alongside). The ReportLab script below is the lightweight Python path when you don't want a Node toolchain. They render the same audit JSON; don't run both.
pip install reportlab../geo/scripts/generate_pdf_report.py (shared with the geo umbrella skill). Run it from the repo root: python3 skills/geo/scripts/generate_pdf_report.py <data.json> <out.pdf>.geo-audit) to have data to include in the reportAfter running a full /geo-audit, collect all scores, findings, and recommendations into a JSON structure. The JSON data must follow this schema:
{
"url": "https://example.com",
"brand_name": "Example Company",
"date": "2026-02-18",
"geo_score": 65,
"scores": {
"ai_citability": 62,
"brand_authority": 78,
"content_eeat": 74,
"technical": 72,
"schema": 45,
"platform_optimization": 59
},
"platforms": {
"Google AI Overviews": 68,
"ChatGPT": 62,
"Perplexity": 55,
"Gemini": 60,
"Bing Copilot": 50
},
"executive_summary": "A 4-6 sentence summary of the audit findings...",
"findings": [
{
"severity": "critical",
"title": "Finding Title",
"description": "Description of the finding and its impact."
}
],
"quick_wins": [
"Action item 1",
"Action item 2"
],
"medium_term": [
"Action item 1",
"Action item 2"
],
"strategic": [
"Action item 1",
"Action item 2"
],
"crawler_access": {
"GPTBot": {"platform": "ChatGPT", "status": "Allowed", "recommendation": "Keep allowed"},
"ClaudeBot": {"platform": "Claude", "status": "Blocked", "recommendation": "Unblock for visibility"}
}
}
Write the collected audit data to a temporary JSON file:
# Write audit data to temp file
cat > /tmp/geo-audit-data.json << 'EOF'
{ ... audit JSON data ... }
EOF
Run the PDF generation script:
python3 skills/geo/scripts/generate_pdf_report.py /tmp/geo-audit-data.json GEO-REPORT-[brand].pdf
The script will produce a professional PDF report with:
After generation, tell the user where the PDF was saved and its file size.
When the user runs this skill, follow this exact sequence:
Check for existing audit data — Look for recent GEO audit reports in the current directory:
GEO-CLIENT-REPORT.mdGEO-AUDIT-REPORT.mdGEO-*.md files from a recent auditIf no audit data exists — Tell the user to run /geo-audit <url> first, then come back for the PDF.
If audit data exists — Parse the markdown report to extract:
Build the JSON — Structure all data into the JSON schema shown above.
Write JSON to temp file — Save to /tmp/geo-audit-data.json
Run the PDF generator:
python3 skills/geo/scripts/generate_pdf_report.py /tmp/geo-audit-data.json "GEO-REPORT-[brand_name].pdf"
Report success — Tell the user the PDF was generated, its location, and file size.
If the user runs /geo-report-pdf https://example.com with a URL:
geo-audit skill for that URLWhen extracting data from existing GEO markdown reports, look for these patterns:
pip install reportlab