Smart Document Pipeline

Quick Reference

# Convert document (auto-caches, auto-summarizes if >100KB)
python ~/.claude/lib/document-converter.py "/path/to/file.pdf"

# Force regenerate
python ~/.claude/lib/document-converter.py "/path/to/file.pdf" --force

# List cached documents
python ~/.claude/lib/document-converter.py --list

# Cleanup old cache (>1 week)
python ~/.claude/lib/document-converter.py --cleanup

Supported Formats

Output Structure

{
  "cache_path": "/path/to/cached/file.md",
  "summary_path": "/path/to/cached/file_summary.md",  // if >100KB
  "from_cache": false,
  "original_size": 26744198,
  "converted_size": 129844,
  "summary_size": 30638,
  "savings_percent": 99.5,
  "recommendation": "summary"  // "summary" or "full"
}

Auto-Summary

Documents >100KB automatically get a summary version:

The summary preserves:

• All headers and structure
• First portion of each section
• Metadata and source reference

Automatic Integration

The smart-read-interceptor hook automatically triggers when you read:

• PDF, Word, PowerPoint, Excel files
• Any file >200KB

It will suggest:

1. Use summary – If summary exists (best for overview)
2. Use cache – If full cached version exists
3. Convert first – If no cache exists
4. Delegate – For very large files, use subagent

Subagent Delegation Pattern

For very large documents, delegate to isolated context:

Task(
  subagent_type="Explore",
  prompt="Read and summarize key points from: /path/to/large-file.pdf.
         Focus on: [specific topics]. Max 500 words summary."
)

This keeps the large content OUT of main context.

Cache Location

~/.claude/cache/documents/
├── filename_hash.md           # Full converted version
├── filename_hash_summary.md   # Summary (if >100KB)
└── ...

Cache expires after 1 week. Run --cleanup to remove old files.

Real-World Results

Workflow Examples

Reading a PDF for research

1. User asks to analyze a PDF
2. Hook detects: "📄 DOCUMENT FILE: .PDF"
3. Convert: python ~/.claude/lib/document-converter.py "file.pdf"
4. Read the summary for overview
5. Read specific sections from full version if needed

Processing multiple documents

1. Convert all documents first (batch):
   for f in *.pdf; do python ~/.claude/lib/document-converter.py "$f"; done

2. Read summaries in main context
3. Delegate deep analysis to subagents