MinerU-Skill
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An AI Skill that transforms PDFs into clean Markdown using MinerU's VLM engine. Supports LaTeX formulas, tables, images, and batch async processing.
README.md
MinerU Skill
An AI Skill that transforms PDF documents into clean Markdown using MinerU's VLM engine.
中文文档 | English
🤖 What is a Skill?
A Skill is an AI capability package that extends your AI assistant's abilities. When you ask the AI to do something, it automatically:
- Recognizes the task from your natural language
- Activates the appropriate skill
- Executes the task using the skill's tools
- Delivers results back to you
Example Conversation
You: 解析这些考研数学真题 PDF 到我的 Obsidian
AI: 📚 发现 40 个 PDF 文件
⏳ 开始并行解析 (5 workers)...
✅ 1993年考研数学(一)真题 → Markdown
✅ 1994年考研数学(一)真题 → Markdown
...
✅ 完成!已保存到 Obsidian/考研/数学一/
🚀 Install as Skill
Vercel Skills (Recommended)
npx skills add Nebutra/MinerU-Skill
Supported: OpenCode, Claude Code, Codex, Cursor, 35+ more
OpenClaw
# Clone to your skills directory
git clone https://github.com/Nebutra/MinerU-Skill.git ~/openclaw-skills/mineru/
# Set API token
export MINERU_TOKEN="your-token-here" # Get from https://mineru.net/user-center/api-token
ClawHub
# Install via clawhub CLI
clawhub install mineru
Claude Code / Cursor / Windsurf
# Clone to AI skills folder
git clone https://github.com/Nebutra/MinerU-Skill.git ~/.claude/skills/mineru/
💬 Usage Examples
Single File
把 ./document.pdf 解析成 Markdown
Batch Directory
解析 ./papers/ 目录下的所有 PDF,输出到 ./output/
Direct to Obsidian
把这些 PDF 解析后直接保存到我的 Obsidian Vault
Chinese Example
解析 1987-2025 年考研数学真题,保存到 Obsidian/考研/数学一/
用 10 个并发,跳过已处理的文件
⚡ Features
| Feature | Description |
|---|---|
| 📄 PDF Input | Local files, URLs, batch directories |
| 📝 Output | Markdown + JSON metadata + Images |
| 🔢 LaTeX | Math formulas preserved |
| 📊 Tables | Structure extraction |
| 🖼️ Images | Auto-extracted to images/ |
| ⚡ Async | 15x parallel uploads |
| 🔄 Resume | Skip processed files |
| 📁 Obsidian | Direct vault output |
🛠️ CLI Reference
You can also use directly via CLI:
# Single file
python scripts/mineru_v2.py --file ./doc.pdf --output ./output/
# Batch with resume
python scripts/mineru_v2.py \
--dir ./pdfs/ \
--output ~/Obsidian/MyVault/ \
--workers 10 \
--resume
| Option | Description |
|---|---|
--dir PATH |
Input directory |
--file PATH |
Single file |
--output PATH |
Output directory |
--workers N |
Concurrency (default: 5) |
--resume |
Skip processed files |
--token TOKEN |
API token |
📁 Output Structure
output/
├── document-name/
│ ├── document-name.md # Main Markdown
│ ├── images/ # Extracted images
│ │ ├── image_0_0.png
│ │ └── ...
│ └── content.json # Metadata
└── ...
📊 Performance
Test: 10 PDFs, ~15 pages each (MacBook Air M1)
| Configuration | Time | Speed |
|---|---|---|
| Sequential | 8.5 min | 1.2 files/min |
| Async (5 workers) | 3.2 min | 3.1 files/min |
| Async (15 workers) | 1.8 min | 5.6 files/min |
🔑 Get API Token
- Visit MinerU
- Create free API token
- Set environment:
export MINERU_TOKEN="your-token-here"
Free Tier: 2000 pages/day, 200MB max file
⭐ Star History
🏗️ Skill Architecture
┌─────────────────────────────────────────────────────────────┐
│ USER REQUEST │
│ "Parse these PDFs to Markdown" │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ AI ASSISTANT │
│ • Recognizes PDF parsing task │
│ • Activates MinerU skill │
│ • Reads SKILL.md for instructions │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ MINERU SKILL ENGINE │
│ Scanner ──► Scheduler ──► Worker Pool (N workers) │
│ │ │
│ ▼ │
│ API: Get URL ──► Upload ──► Poll ──► Download │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ OUTPUT │
│ Markdown + JSON + Images ──► Obsidian/Files │
└─────────────────────────────────────────────────────────────┘
🤝 Contributing
- Fork → Branch → Commit → Push → PR
📝 License
MIT License - see LICENSE
🙏 Acknowledgments
If this skill helps you, give it a ⭐!
Made with ❤️ by Nebutra
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