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Agent Skill · 研究检索

pdf-processing(PDF processing 文档)

pdf-processing 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

pdf-processing 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 69,522。

总安装 218周安装 9GitHub Stars 公开资料未说明下载 71核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pdf-processing(PDF processing 文档)
来源仓库:https://github.com/wesley1600/claudecodeframework
仓库路径:skills/pdf-processing
安装命令:
npx skills add https://github.com/wesley1600/claudecodeframework --skill pdf-processing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/wesley1600/claudecodeframework --skill pdf-processing

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

PDF processing 文档用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它支持基于关键词或任务场景进行信息检索,适用于文档处理场景。通过 npx skills add 命令从指定仓库安装,需结合原始 README 确认具体用法。

使用前应核实权限范围、维护状态,避免触发联网或文件读写操作。pdf-processing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/pdf-processing

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/wesley1600/claudecodeframework --skill pdf-pro…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

pdf-processing 怎么安装?

在终端执行 npx skills add https://github.com/wesley1600/claudecodeframework --skill pdf-processing。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pdf-processing 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

pdf-processing 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

pdf-processing 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

pdf-processing 用的人多吗?

公开数据是累计安装 218 次,近一周安装 9 次,下载 71 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

pdf-processing 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
pdf-processing

PDF processing 文档 · 本页

218—需要联网未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

PDF Processing Skill

Overview

This skill provides comprehensive PDF processing capabilities leveraging Claude's native PDF support, which can:

  • Extract and parse text content
  • Identify and extract tables with structure preservation
  • Analyze charts, graphs, and visual elements
  • Understand document layout and formatting
  • Generate summaries and insights
  • Convert PDFs to various formats (Markdown, JSON, CSV, plain text)

When to Use

Claude should automatically activate this skill when:

  • User provides a PDF file path or wants to process a PDF
  • User asks to extract text, tables, or data from PDFs
  • User requests PDF analysis, summarization, or conversion
  • User needs to understand charts, diagrams, or visual content in PDFs
  • User wants to transform PDF content to another format

Key Capabilities

1. Text Extraction

  • Full document text extraction with formatting preservation
  • Page-by-page text extraction
  • Section and paragraph identification
  • Header and footer detection

2. Table Extraction

  • Automatic table detection and extraction
  • Structure-preserving conversion to CSV, JSON, or Markdown
  • Multi-page table handling
  • Cell merging and complex table support

3. Visual Content Analysis

  • Chart and graph interpretation
  • Diagram and flowchart understanding
  • Image and figure description
  • Infographic analysis

4. Document Understanding

  • Layout analysis and structure detection
  • Multi-column text handling
  • Form field identification
  • Metadata extraction (title, author, creation date, page count)

5. Format Conversion

  • PDF to Markdown (preserving headings, lists, tables)
  • PDF to JSON (structured data extraction)
  • PDF to CSV (table extraction)
  • PDF to plain text

Instructions

Step 1: Validate PDF Input

First, determine how the PDF is provided:

Option A: File Path

# Verify the file exists and is a PDF
ls -lh /path/to/document.pdf
file /path/to/document.pdf

Option B: Base64-Encoded PDF If the user provides base64-encoded content, save it first:

# Decode and save base64 PDF
python .claude/skills/pdf-processing/scripts/decode_pdf.py --input base64_string.txt --output document.pdf

Option C: URL If the user provides a URL, download it:

# Download PDF from URL
python .claude/skills/pdf-processing/scripts/download_pdf.py --url "https://example.com/doc.pdf" --output document.pdf

Step 2: Read and Analyze PDF

Use the Read tool to access PDF files. Claude's native PDF support will:

  • Display the PDF content visually
  • Extract text and structure automatically
  • Identify tables, charts, and images
# Example: Read PDF using the Read tool
# The Read tool handles PDFs natively and extracts text + visual content
Read(file_path="/absolute/path/to/document.pdf")

Step 3: Process Based on User Request

A. Text Extraction

For simple text extraction:

python .claude/skills/pdf-processing/scripts/extract_text.py \
  --input document.pdf \
  --output document.txt \
  --preserve-formatting true

For page-specific extraction:

python .claude/skills/pdf-processing/scripts/extract_text.py \
  --input document.pdf \
  --pages 1,3,5-10 \
  --output selected_pages.txt
B. Table Extraction

Extract all tables to CSV:

python .claude/skills/pdf-processing/scripts/extract_tables.py \
  --input document.pdf \
  --format csv \
  --output-dir ./extracted_tables/

Extract tables to JSON with structure:

python .claude/skills/pdf-processing/scripts/extract_tables.py \
  --input document.pdf \
  --format json \
  --output tables.json
C. Document Summarization

Generate a summary of the PDF:

python .claude/skills/pdf-processing/scripts/summarize_pdf.py \
  --input document.pdf \
  --output summary.md \
  --style concise  # Options: concise, detailed, executive
D. Format Conversion

Convert PDF to Markdown:

python .claude/skills/pdf-processing/scripts/convert_pdf.py \
  --input document.pdf \
  --output document.md \
  --format markdown \
  --preserve-images true

Convert PDF to structured JSON:

python .claude/skills/pdf-processing/scripts/convert_pdf.py \
  --input document.pdf \
  --output document.json \
  --format json \
  --extract-metadata true
E. Visual Content Analysis

Analyze charts and graphs:

python .claude/skills/pdf-processing/scripts/analyze_visuals.py \
  --input document.pdf \
  --output analysis.json \
  --elements charts,graphs,diagrams

Step 4: Post-Processing and Output

After extraction/conversion:

  1. Validate Output: Check that the output file was created successfully
ls -lh output_file.{txt,md,json,csv}
  1. Preview Results: Show the user a preview of the extracted content
head -n 20 output_file.txt  # For text files
cat output_file.json | python -m json.tool | head -n 50  # For JSON
  1. Provide Summary: Summarize what was extracted and offer next steps

Step 5: Handle Edge Cases

Password-Protected PDFs
python .claude/skills/pdf-processing/scripts/extract_text.py \
  --input document.pdf \
  --password "user_provided_password" \
  --output document.txt
Scanned PDFs (OCR Required)
# Use OCR for scanned PDFs
python .claude/skills/pdf-processing/scripts/ocr_pdf.py \
  --input scanned_document.pdf \
  --output document.txt \
  --language eng  # Language code: eng, fra, deu, etc.
Large PDFs (Memory Optimization)
# Process large PDFs in chunks
python .claude/skills/pdf-processing/scripts/extract_text.py \
  --input large_document.pdf \
  --output document.txt \
  --chunk-size 10  # Process 10 pages at a time

Error Handling

Common Issues

  1. File Not Found

- Verify the path with ls or Glob - Check for typos in the filename - Ensure absolute paths are used

  1. Corrupted PDF

- Try reading with the Read tool first - Use repair mode: python scripts/repair_pdf.py --input corrupted.pdf --output repaired.pdf

  1. Unsupported PDF Features

- Some PDFs with complex DRM or encryption may fail - Inform the user and suggest alternatives

  1. OCR Failures

- Check if tesseract is installed: which tesseract - Verify image quality is sufficient - Try different language settings

Best Practices

  1. Always use the Read tool first - This leverages Claude's native PDF support for best results
  2. Preserve structure - When extracting tables or converting formats, maintain the original structure
  3. Validate outputs - Always check that output files were created successfully
  4. Provide context - Tell the user what was extracted and what they can do next
  5. Handle errors gracefully - If processing fails, explain why and suggest alternatives
  6. Respect privacy - Remind users not to upload sensitive documents without proper authorization

Output Formats

Text Output

Plain text with optional formatting preservation
Line breaks and paragraphs maintained
Special characters preserved

Markdown Output

# Document Title

## Section Heading

Paragraph text with **bold** and *italic* formatting.

| Column 1 | Column 2 | Column 3 |
|----------|----------|----------|
| Data 1   | Data 2   | Data 3   |

![Chart Description](chart_extracted.png)

JSON Output

{
  "metadata": {
    "title": "Document Title",
    "author": "Author Name",
    "pages": 42,
    "creation_date": "2024-01-15"
  },
  "content": [
    {
      "page": 1,
      "type": "text",
      "content": "Page 1 text content..."
    },
    {
      "page": 2,
      "type": "table",
      "headers": ["Col1", "Col2", "Col3"],
      "rows": [["A", "B", "C"], ["D", "E", "F"]]
    }
  ]
}

CSV Output (for tables)

Column1,Column2,Column3
Value1,Value2,Value3
Value4,Value5,Value6

Advanced Features

Batch Processing

# Process multiple PDFs
python .claude/skills/pdf-processing/scripts/batch_process.py \
  --input-dir ./pdfs/ \
  --output-dir ./extracted/ \
  --format markdown

Custom Templates

# Use custom conversion templates
python .claude/skills/pdf-processing/scripts/convert_pdf.py \
  --input document.pdf \
  --output document.md \
  --template .claude/skills/pdf-processing/assets/custom_template.md

Selective Extraction

# Extract only specific sections
python .claude/skills/pdf-processing/scripts/extract_sections.py \
  --input document.pdf \
  --sections "Introduction,Methods,Results" \
  --output extracted_sections.md

Integration with Other Tools

This skill works well with:

  • Data analysis tools - Extract tables and feed to pandas/numpy
  • Documentation generators - Convert PDFs to Markdown for wikis
  • Search systems - Extract text for indexing
  • Automation workflows - Batch process invoices, reports, forms

Examples

Example 1: Extract and Summarize

# User: "Please extract the key points from this research paper"
1. Read(file_path="/path/to/paper.pdf")
2. python scripts/summarize_pdf.py --input paper.pdf --output summary.md --style executive
3. Show the user the summary with key findings highlighted

Example 2: Extract Tables to CSV

# User: "Get all tables from this financial report"
1. Read(file_path="/path/to/report.pdf")
2. python scripts/extract_tables.py --input report.pdf --format csv --output-dir ./tables/
3. List the extracted CSV files and preview the first table

Example 3: Convert to Markdown

# User: "Convert this PDF to markdown"
1. Read(file_path="/path/to/document.pdf")
2. python scripts/convert_pdf.py --input document.pdf --output document.md --format markdown
3. Show preview of the markdown and confirm successful conversion

Dependencies

The scripts in this skill require:

  • Python 3.8+
  • PyPDF2 or pypdf (PDF parsing)
  • pdfplumber (table extraction)
  • pdf2image (image extraction)
  • pytesseract (OCR for scanned PDFs)
  • Pillow (image processing)
  • requests (URL downloads)

These are installed via the requirements file in assets/requirements.txt.

References

See the references/ directory for:

  • pdf_capabilities.md - Detailed breakdown of Claude's PDF support
  • api_reference.md - Complete API documentation for all scripts
  • examples.md - More usage examples and use cases
  • troubleshooting.md - Common issues and solutions

Notes for Claude

  • Always read PDFs with the Read tool first - This is the most reliable method
  • After reading, analyze what the user needs - Text, tables, summary, conversion?
  • Use the appropriate script - Don't try to do everything manually
  • Validate outputs - Always check that files were created successfully
  • Provide helpful context - Explain what was extracted and suggest next steps
  • Handle errors gracefully - If something fails, explain why and offer alternatives
  • Be efficient - Use batch processing for multiple PDFs
  • Preserve structure - Maintain document formatting when converting

Version History

  • 1.0.0 (2025-11-18) - Initial release with core PDF processing capabilities

平台分布

Codex

34.15%
按下载量换算24

Claude

30.14%
按下载量换算21

Cursor

16.33%
按下载量换算12

Gemini CLI

9.65%
按下载量换算7

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

未通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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