Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
pdf-parser-mineru
PDF document parsing tool based on local MinerU, supports converting PDF to Markdown, JSON, and other machine-readable formats.
skill openclawclawhub Free
PDF document parsing tool based on local MinerU, supports converting PDF to Markdown, JSON, and other machine-readable formats.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- install.sh, SKILL.md, SKILL_zh.md, script/pdf_parser.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- baokui
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
1. pdf_to_markdown
Convert PDF documents to Markdown format, preserving document structure, formulas, tables, and images. Description: Use MinerU to parse PDF documents and output in Markdown format, supporting OCR, formula recognition, table extraction, and other features. Parameters: file_path (string, required): Absolute path to the PDF file output_dir (string, required): Absolute path to the output directory backend (string, optional): Parsing backend, options: hybrid-auto-engine (default), pipeline, vlm-auto-engine language (string, optional): OCR language code, such as en (English), ch (Chinese), ja (Japanese), etc., defaults to auto-detection enable_formula (boolean, optional): Whether to enable formula recognition, defaults to true enable_table (boolean, optional): Whether to enable table extraction, defaults to true start_page (integer, optional): Start page number (starting from 0), defaults to 0 end_page (integer, optional): End page number (starting from 0), defaults to -1 meaning parse all pages Return Value: { "success": true, "output_path": "/path/to/output", "markdown_content": "Converted Markdown content...", "images": ["List of image paths"], "tables": ["List of table information"], "formula_count": 10 } Examples: python .claude/skills/pdf-process/script/pdf_parser.py \ '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output"}}' # Use specific backend python .claude/skills/pdf-process/script/pdf_parser.py \ '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "backend": "pipeline"}}' # Parse specific pages python .claude/skills/pdf-process/script/pdf_parser.py \ '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "start_page": 0, "end_page": 5}}'
2. pdf_to_json
Convert PDF documents to JSON format, including detailed layout and structural information. Description: Use MinerU to parse PDF documents and output in JSON format, containing structured information such as text blocks, images, tables, formulas, etc. Parameters: file_path (string, required): Absolute path to the PDF file output_dir (string, required): Absolute path to the output directory backend (string, optional): Parsing backend, options: hybrid-auto-engine (default), pipeline, vlm-auto-engine language (string, optional): OCR language code, such as en (English), ch (Chinese), ja (Japanese), etc., defaults to auto-detection enable_formula (boolean, optional): Whether to enable formula recognition, defaults to true enable_table (boolean, optional): Whether to enable table extraction, defaults to true start_page (integer, optional): Start page number (starting from 0), defaults to 0 end_page (integer, optional): End page number (starting from 0), defaults to -1 meaning parse all pages Return Value: { "success": true, "output_path": "/path/to/output.json", "pages": [ { "page_no": 0, "page_size": [595, 842], "blocks": [ { "type": "text", "text": "Text content", "bbox": [x, y, x, y] } ], "images": [], "tables": [], "formulas": [] } ], "metadata": { "total_pages": 10, "author": "Author", "title": "Title" } } Examples: python .claude/skills/pdf-process/script/pdf_parser.py \ '{"name": "pdf_to_json", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output"}}' # Use specific backend and language python .claude/skills/pdf-process/script/pdf_parser.py \ '{"name": "pdf_to_json", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "backend": "hybrid-auto-engine", "language": "ch"}}'
1. Install MinerU
# Update pip and install uv pip install --upgrade pip pip install uv # Install MinerU (including all features) uv pip install -U "mineru[all]"
2. Verify Installation
# Check if MinerU is installed successfully mineru --version # Test basic functionality mineru --help
3. System Requirements
Python Version: 3.10-3.13 Operating System: Linux / Windows / macOS 14.0+ Memory: Using pipeline backend: minimum 16GB, recommended 32GB+ Using hybrid/vlm backend: minimum 16GB, recommended 32GB+ Disk Space: minimum 20GB (SSD recommended) GPU (optional): pipeline backend: supports CPU-only hybrid/vlm backend: requires NVIDIA GPU (Volta architecture and above) or Apple Silicon
Use Cases
Academic Paper Parsing: Extract structured content such as formulas, tables, and images Technical Document Conversion: Convert PDF documents to Markdown for version control and online publishing OCR Processing: Process scanned PDFs and garbled PDFs Multilingual Documents: Supports OCR recognition for 109 languages Batch Processing: Batch convert multiple PDF documents
Backend Selection Recommendations
hybrid-auto-engine (default): Balanced accuracy and speed, suitable for most scenarios pipeline: Suitable for CPU-only environments, best compatibility vlm-auto-engine: Highest accuracy, requires GPU acceleration
Notes
File Paths: All paths must be absolute paths Output Directory: Non-existent directories will be created automatically Performance: Using GPU can significantly improve parsing speed Page Numbers: Page numbers start counting from 0 Memory: Processing large documents may consume more memory
Common Issues
Installation Failure: Ensure using Python 3.10-3.13 Windows only supports Python 3.10-3.12 (ray does not support 3.13) Using uv pip install can resolve most dependency conflicts Insufficient Memory: Use pipeline backend Limit parsing pages: start_page and end_page Reduce virtual memory allocation Slow Parsing Speed: Enable GPU acceleration Use hybrid-auto-engine backend Disable unnecessary features (formulas, tables) Low OCR Accuracy: Specify the correct document language Ensure the backend supports OCR (use pipeline or hybrid-*)
Related Resources
MinerU Official Documentation: https://opendatalab.github.io/MinerU/ MinerU GitHub: https://github.com/opendatalab/MinerU Online Demo: https://mineru.net/
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts
- SKILL.md
- SKILL_zh.md
- install.sh
- script/pdf_parser.py