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document-conversion

/document-conversion

This skill should be used when users need to convert PDFs (especially with tables or figures), mentions 'convert', 'PDF', 'document processing', has complex academic papers to import, or asks about document conversion options.

Licence
MIT
Last repository change
13.07.2026

Assessment and security

100/ 100Class: good
Format25 / 25
Frontmatter with name and description, valid name, description up to 1,024 characters, name matching the folder.
Discoverability25 / 25
Description explains when to use the skill, has a reasonable length and uses third person.
Content35 / 35
At least 150 words and at most 500 lines, headings, step lists, examples or code.
Hygiene15 / 15
No placeholders such as TODO, no Windows paths, with a licence.
Riskno signals detected in the scan
Files in this package
1
Scripts
0
HTML comments
0
URLs in SKILL.md
2
Deprecation

No signals detected. Repository changed on 13.07.2026, without references to retired models or APIs.

Licence

MIT, open: use and modify while retaining author attribution.

What it does and when to use it

This skill should be used when users need to convert PDFs (especially with tables or figures), mentions 'convert', 'PDF', 'document processing', has complex academic papers to import, or asks about document conversion options.

Triggers from the description

convertPDFdocument processing
Original author description

This skill should be used when users need to convert PDFs (especially with tables or figures), mentions 'convert', 'PDF', 'document processing', has complex academic papers to import, or asks about document conversion options.

How it works

  1. When to Use
  2. Conversion Options
  3. When to Use Which
  4. Tool Selection Logic
  5. Usage Examples
  6. MinerU Specific Features
  7. Output Quality Checklist
  8. Integration with Research Workflow
  9. Fallback Options
  10. Related
Original SKILL.md headings · 152 lines, 623 words
SKILL.md preview
---
name: document-conversion
description: "This skill should be used when users need to convert PDFs (especially with tables or figures), mentions 'convert', 'PDF', 'document processing', has complex academic papers to import, or asks about document conversion options."
---

# 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

| Feature | MinerU (Optional) | Manual Conversion |
|---------|-------------------|-------------------|
| API Key Required | Yes (MINERU_API_KEY) | No |
| PDF Accuracy | 90%+ (VLM mode) | Varies |
| Table Extraction | Excellent | Manual cleanup |
| Figure Handling | Extracts + describes | Manual description |
| Formula Recognition | Yes | Limited |
| Multi-column | Excellent | Manual formatting |
| Cost | Pay per page | Free |

## 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
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

Versions

RepositoryScoreLicence
This page · linxule/interpretive-orchestration100MIT
codealta/codealta100BSD-2-Clause
nimadorostkar/Claude-Skills-collection100MIT
athola/claude-night-market100MIT
linxule/interpretive-orchestration100MIT

Database snapshot: 13.11.2026 · package version e867201a27c6 (SHA-256 digest of the entire package)

Downloads in the last 30 days: 0 · ranking

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