/geo-report-pdf

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

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.

> **Two PDF paths — pick one.** For a **branded, client-ready** deliverable, prefer the TypeScript [`tools/audit-report-kit`](../../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.

Prerequisites

• **ReportLab** must be installed: `pip install reportlab`
• The Python PDF generation script lives at [`../geo/scripts/generate_pdf_report.py`](../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>`.
• 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 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:

• **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 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

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", "C