linxule/interpretive-orchestration/document-conversion

document-conversion

Robust PDF and document conversion with intelligent tool selection. Chooses the best available conversion method based on document complexity and MCP availability.

When to Use

Use this skill when:

• User needs to convert PDFs, especially with tables or figures
• User mentions "convert", "PDF", "document processing"
• User has complex academic papers to import
• User asks about document conversion options

Conversion Options

When to Use Which

Use MinerU When:

• PDF has complex tables with merged cells
• Document has multi-column layouts
• Figures/charts need extraction
• Mathematical formulas present
• Academic paper with structured formatting
• Accuracy is critical

Use Manual Conversion When:

• Simple text-based documents
• MinerU API key not available
• Cost is a concern
• Document is straightforward

Tool Selection Logic

Is the document a PDF with tables/figures?
├── Yes, complex tables
│   └── MinerU available?
│       ├── Yes → Use MinerU (vlm mode)
│       └── No → Manual conversion + review
├── Yes, simple formatting
│   └── Manual conversion or external tool
└── No, other format
    └── Is it audio?
        ├── Yes → External transcription service
        └── No → Manual conversion

Usage Examples

MinerU (Complex PDF)

Use mineru_parse to convert this academic paper:
- URL: https://example.com/paper.pdf
- Model: vlm (for 90% accuracy)
- Enable: formula, table recognition

Manual Conversion (Simple Document)

For simple PDFs without MinerU:
1. Use Adobe Acrobat to export to Word/text
2. Or open in Google Docs for auto-OCR
3. Review and clean up formatting

Batch Processing

For multiple PDFs:
1. Check which have complex tables (use MinerU if available)
2. Process simple ones with manual conversion
3. Queue complex ones for MinerU batch if API key available

MinerU Specific Features

VLM vs Pipeline Mode

• VLM Mode: Uses vision-language model, 90%+ accuracy, slower
• Pipeline Mode: Traditional parsing, faster, lower accuracy

Page Selection

Parse only specific pages:
mineru_parse({
  url: "https://...",
  pages: "1-10,15,20-25"
})

Batch Processing

Process multiple documents:
mineru_batch({
  urls: ["url1", "url2", "url3"],
  model: "vlm"
})

Output Quality Checklist

After conversion, verify:

• [ ] Text is accurately extracted
• [ ] Tables maintain structure
• [ ] Headers/sections are correct
• [ ] Figures have descriptions (if MinerU)
• [ ] Formulas are readable (if MinerU)
• [ ] No garbled text from OCR errors

Integration with Research Workflow

For Literature (Stream A)

1. Identify papers to convert
2. Complex papers → MinerU (if API key available)
3. Simple papers → Manual conversion
4. Store in stream-a-theoretical/papers/

For Data Documents (Stream B)

1. Interview transcripts → External service (Otter.ai, Rev.com)
2. PDF field notes → MinerU or manual conversion
3. Store in appropriate stage folder

Fallback Options

If MinerU unavailable:

1. Adobe Acrobat - Export to Word
2. Google Docs - Open PDF for auto-OCR
3. Tesseract OCR - Command-line tool
4. Manual transcription - Last resort

Audio Transcription

For audio files, use external services:

• Otter.ai - Good transcription service
• Rev.com - Professional transcription
• YouTube - Upload as unlisted video for auto-captions

Related

• MCPs: MinerU (optional)
• Skills: interview-ingest for audio, literature-sweep for papers
• Configuration: .mcp.json defines MCP availability