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University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
Parse PDF documents with MinerU MCP to extract text, tables, and formulas. Supports multiple backends including MLX-accelerated inference on Apple Silicon.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install etoile04/mineru-pdf打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Parse PDF documents using MinerU MCP to extract structured content including text, tables, and formulas with MLX acceleration on Apple Silicon.
claude mcp add --transport stdio --scope user mineru -- \
uvx --from mcp-mineru python -m mcp_mineru.server
This installs and configures MinerU for all Claude projects. Models are downloaded on first use.
The skill includes a direct parsing tool that saves output to a persistent directory:
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py <pdf_path> <output_dir> [options]
Advantages:
# Parse entire PDF
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output"
# Parse specific pages
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--start-page 0 --end-page 2
# Use Apple Silicon optimization
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--backend vlm-mlx-engine
# Text only (faster)
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--no-table --no-formula
uvx --from mcp-mineru python -c "
import asyncio
from mcp_mineru.server import call_tool
async def parse_pdf():
result = await call_tool(
name='parse_pdf',
arguments={
'file_path': '/path/to/document.pdf',
'backend': 'pipeline',
'formula_enable': True,
'table_enable': True,
'sta...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# MinerU PDF Parser
Parse PDF documents using MinerU MCP to extract structured content including text, tables, and formulas with MLX acceleration on Apple Silicon.
## Installation
### Option 1: Install MinerU MCP (for Claude Code)
```bash
claude mcp add --transport stdio --scope user mineru -- \
uvx --from mcp-mineru python -m mcp_mineru.server
```
This installs and configures MinerU for all Claude projects. Models are downloaded on first use.
### Option 2: Use Direct Tool (preserves files)
The skill includes a direct parsing tool that saves output to a persistent directory:
```bash
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py <pdf_path> <output_dir> [options]
```
**Advantages:**
- ✅ Files are saved permanently (not auto-deleted)
- ✅ Full control over output location
- ✅ No MCP overhead
- ✅ Works with any Python environment that has MinerU
## Quick Start
### Method 1: Using the Direct Tool (Recommended)
```bash
# Parse entire PDF
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output"
# Parse specific pages
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--start-page 0 --end-page 2
# Use Apple Silicon optimization
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--backend vlm-mlx-engine
# Text only (faster)
python /Users/lwj04/clawd/skills/mineru-pdf/parse.py \
"/path/to/document.pdf" \
"/path/to/output" \
--no-table --no-formula
```
### Method 2: Using MinerU MCP (Temporary Files)
### Parse a PDF document
```bash
uvx --from mcp-mineru python -c "
import asyncio
from mcp_mineru.server import call_tool
async def parse_pdf():
result = await call_tool(
name='parse_pdf',
arguments={
'file_path': '/path/to/document.pdf',
'backend': 'pipeline',
'formula_enable': True,
'table_enable': True,
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