GEO PDF Report Generator

Purpose

Prerequisites

• ReportLab must be installed:

  pip install reportlab

• The PDF generation script is located at:

  ~/.claude/skills/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

/geo-audit

{
    "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 audit data to temp file
cat > /tmp/geo-audit-data.json << 'EOF'
{ ... audit JSON data ... }
EOF

Step 3: Generate the PDF

python3 ~/.claude/skills/geo/scripts/generate_pdf_report.py /tmp/geo-audit-data.json GEO-REPORT-[brand].pdf

• 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

Complete Workflow Example

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:
   bash

   python3 ~/.claude/skills/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

/geo-report-pdf https://example.com

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

• 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)