doc2md

---
name: doc2md
description: Converts binary documents (PDF, DOCX, PPTX, XLSX, EPUB, etc) and URLs (YouTube, Wikipedia) to clean Markdown locally — zero LLM tokens. Integrates with graphify and whisper for a full local pipeline.
trigger: /doc2md
---

# /doc2md

Local document-to-markdown converter. Zero LLM tokens spent on conversion.

## Default behavior

**No arguments:**
1. Processes `.raw-docs/` → saves output to `docs/`
2. Only converts new or changed files (MD5 cache)
3. Audio files (.mp3, .wav) → tells user to use `/whisper`

**With arguments:** follows the explicit command.

## Usage

```bash
/doc2md                            # .raw-docs/ → docs/ (default)
/doc2md <file.pdf>                 # single file
/doc2md <folder>                   # entire folder (recursive)
/doc2md --url <url>                # YouTube, Wikipedia, HTML
/doc2md <folder> --watch           # auto-convert on new files
/doc2md <folder> --out <dir>       # custom output folder
/doc2md <file> --force             # skip cache
/doc2md <folder> --list            # show cache status
```

## Supported formats

`.pdf` `.docx` `.pptx` `.xlsx` `.xls` `.csv` `.epub` `.ipynb` `.msg` `.zip`

URLs: YouTube (full transcript), Wikipedia, generic HTML.

## Steps when invoked

### 1. Check dependency
```bash
python -c "from markitdown import MarkItDown; print('ok')"
```
If missing: `pip install markitdown[all]`

### 2. Run the converter
```bash
python ~/.claude/skills/doc2md/scripts/convert.py <input> [flags]
```
Returns JSON. Parse and display as a results table.

### 3. Report to user
```
✓ report.pdf  → report.md  (2,309 words, ~1,732 tokens saved)
⚠️ memo.pdf   (cached, 587 words)
✗ audio.mp3   → use /whisper
```

## graphify integration

```bash
/doc2md           # 0 tokens: convert docs to .md locally
/graphify docs/   # reads .md instead of PDF: 60-80% fewer tokens
```