geo-report-pdf

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Chinese

GEO PDF Report Generator

GEO PDF报告生成器

Purpose

用途

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.

本技能可从GEO审计数据生成专业、视觉精美的PDF报告。该PDF包含评分仪表盘、条形图、平台就绪度可视化、颜色编码表格以及优先级行动计划——可直接交付给客户。

Prerequisites

前置条件

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

必须安装ReportLab：
```
pip install reportlab
```

PDF生成脚本位于：

~/.claude/skills/geo/scripts/generate_pdf_report.py

需先运行完整的GEO审计（使用
```
/geo-audit
```
命令），以获取可纳入报告的数据

How to Generate a PDF Report

如何生成PDF报告

Step 1: Collect Audit Data

步骤1：收集审计数据

After running a full

/geo-audit

, collect all scores, findings, and recommendations into a JSON structure. The JSON data must follow this schema:

json

{
    "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"}
    }
}

运行完整的

/geo-audit

命令后，将所有评分、发现和建议整理为JSON结构。JSON数据必须遵循以下 schema：

json

{
    "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

步骤2：将JSON数据写入临时文件

Write the collected audit data to a temporary JSON file:

bash

undefined

将收集到的审计数据写入临时JSON文件：

bash

undefined

Write audit data to temp file

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

undefined

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

undefined

Step 3: Generate the PDF

步骤3：生成PDF

Run the PDF generation script:

bash

python3 ~/.claude/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

运行PDF生成脚本：

bash

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

该脚本将生成一份专业PDF报告，包含以下内容：

封面 — 品牌名称、URL、日期、整体GEO评分及可视化仪表盘
执行摘要 — 关键发现和顶级建议
评分细分 — 6个评分类别的表格和条形图
AI平台就绪度 — 各平台的可视化水平条形图及评分
AI爬虫访问权限 — 颜色编码表格（绿色=允许，红色=阻止）
关键发现 — 按严重程度编码的发现列表（严重/高/中/低）
优先级行动计划 — 快速见效项、中期项和战略举措
附录 — 方法论、数据源和术语表

Step 4: Return the PDF Path

步骤4：返回PDF路径

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

生成完成后，告知用户PDF的保存位置和文件大小。

Complete Workflow Example

完整工作流示例

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.md
```
- ```
GEO-AUDIT-REPORT.md
```
- Or any
```
GEO-*.md
```
  files from a recent audit
If 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:
- 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
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:

bash

python3 ~/.claude/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.

当用户运行本技能时，请严格遵循以下步骤：

检查现有审计数据 — 在当前目录中查找近期的GEO审计报告：
- ```
GEO-CLIENT-REPORT.md
```
- ```
GEO-AUDIT-REPORT.md
```
- 任何近期审计生成的
```
GEO-*.md
```
  文件
若无审计数据 — 告知用户需先运行
```
/geo-audit <url>
```
命令，之后再返回生成PDF。
若有审计数据 — 解析markdown报告以提取以下内容：
- 整体GEO评分
- 类别评分（可引用性、品牌权威性、内容E-E-A-T、技术、Schema、平台）
- 平台就绪度评分（Google AI Overviews、ChatGPT、Perplexity、Gemini、Bing Copilot）
- AI爬虫访问状态
- 带严重级别的关键发现
- 快速见效项、中期项和战略行动项
- 执行摘要
构建JSON — 将所有数据整理为上述所示的JSON schema结构。
将JSON写入临时文件 — 保存至
```
/tmp/geo-audit-data.json
```

运行PDF生成器：

bash

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

报告成功 — 告知用户PDF已生成，以及其保存位置和文件大小。

If the User Provides a URL

若用户提供URL

If the user runs

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

with a URL:

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

当用户运行

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

并附带URL时：

首先运行完整审计：调用
```
geo-audit
```
技能处理该URL
然后从生成的报告文件中收集所有审计数据
按照上述步骤生成PDF

Parsing Markdown Audit Data

解析Markdown审计数据

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"

从现有GEO markdown报告提取数据时，请查找以下模式：

GEO评分：查找包含"GEO Score: XX/100"、"Overall: XX/100"或"GEO Readiness Score: XX"的内容
类别评分：查找包含“Component | Score | Weight”等列的评分表格
平台评分：查找包含“Google AI Overviews”、“ChatGPT”、“Perplexity”等项的表格
爬虫状态：查找包含GPTBot、ClaudeBot等爬虫的“Allowed”或“Blocked”状态的表格
发现：查找标题为“Key Findings”、“Critical Issues”、“Recommendations”的章节
行动项：查找标题为“Quick Wins”、“Action Plan”、“Recommendations”的章节

Notes

注意事项

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)

若未安装ReportLab，请运行：
```
pip install reportlab
```
PDF采用美国信纸尺寸（8.5" x 11"）
调色板：海军蓝主色（#1a1a2e）、蓝色强调色（#0f3460）、珊瑚色高亮（#e94560）、绿色成功色（#00b894）
每页都有页眉线、页码、“Confidential”（保密）水印和生成日期
评分仪表盘使用交通灯颜色：绿色（80分及以上）、蓝色（60-79分）、黄色（40-59分）、红色（40分以下）