GEO PDF Report Generator

Purpose

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.

Prerequisites

• ReportLab must be installed: pip install reportlab
• The PDF generation script ships with the geo skill: ${CLAUDE_SKILL_DIR}/../geo/scripts/generate_pdf_report.py
• Run a full GEO audit first (using /geo-audit) to have data to include in the report

How to Generate a PDF Report

Step 1: Collect Audit Data

After 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"}
    }
}

Step 2: Write JSON Data to a Temp File

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

Step 3: Generate the PDF

Run the PDF generation script:

python3 "${CLAUDE_SKILL_DIR}/../geo/scripts/generate_pdf_report.py" /tmp/geo-audit-data.json GEO-REPORT-[brand].pdf

The script will produce a professional PDF report with:

• Cover Page — Brand name, URL, date, overall GEO score with visual gauge
• Executive Summary — Key findings and top recommendations
• Score Breakdown — Table and bar chart of all 6 scoring categories
• AI Platform Readiness — Visual horizontal bar chart per platform with scores
• AI Crawler Access — Color-coded table (green=allowed, red=blocked)
• Key Findings — Severity-coded findings list (critical/high/medium/low)
• Prioritized Action Plan — Quick wins, medium-term, and strategic initiatives
• Appendix — Methodology, data sources, and glossary

Step 4: Return the PDF Path

After generation, tell the user where the PDF was saved and its file size.

Complete Workflow Example

When the user runs this skill, follow this exact sequence:

1. Check for existing audit data — Look for recent GEO audit reports in the current directory:
   • GEO-CLIENT-REPORT.md
   • GEO-AUDIT-REPORT.md
   • Or any GEO-*.md files from a recent audit
2. If no audit data exists — Tell the user to run /geo-audit <url> first, then come back for the PDF.
3. If audit data exists — Parse the markdown report to extract:
   • Overall GEO score
   • Category scores (citability, brand authority, content/E-E-A-T, technical, schema, platform)
   • Platform readiness scores (Google AIO, ChatGPT, Perplexity, Gemini, Bing Copilot)
   • AI crawler access status
   • Key findings with severity levels
   • Quick wins, medium-term, and strategic action items
   • Executive summary
4. Build the JSON — Structure all data into the JSON schema shown above.
5. Write JSON to temp file — Save to /tmp/geo-audit-data.json
6. Run the PDF generator:

   python3 "${CLAUDE_SKILL_DIR}/../geo/scripts/generate_pdf_report.py" /tmp/geo-audit-data.json "GEO-REPORT-[brand_name].pdf"

7. Report success — Tell the user the PDF was generated, its location, and file size.

If the User Provides a URL

If the user runs /geo-report-pdf https://example.com with a URL:

1. First run a full audit: invoke the geo-audit skill for that URL
2. Then collect all the audit data from the generated report files
3. Generate the PDF as described above

Parsing Markdown Audit Data

When extracting data from existing GEO markdown reports, look for these patterns:

• GEO Score: Look for "GEO Score: XX/100" or "Overall: XX/100" or "GEO Readiness Score: XX"
• Category Scores: Look for score tables with columns like "Component | Score | Weight"
• Platform Scores: Look for tables with "Google AI Overviews", "ChatGPT", "Perplexity", etc.
• Crawler Status: Look for tables with "Allowed" or "Blocked" status for crawlers like GPTBot, ClaudeBot
• Findings: Look for sections titled "Key Findings", "Critical Issues", "Recommendations"
• Action Items: Look for sections titled "Quick Wins", "Action Plan", "Recommendations"

Notes

• If ReportLab is not installed, run: pip install reportlab
• The PDF is designed for US Letter size (8.5" x 11")
• Color palette: Navy primary (#1a1a2e), Blue accent (#0f3460), Coral highlight (#e94560), Green success (#00b894)
• Each page has a header line, page numbers, "Confidential" watermark, and generation date
• Score gauges use traffic-light colors: green (80+), blue (60-79), yellow (40-59), red (below 40)