PDF Reader

Extract text from PDF files using open-source tools. PyMuPDF (fitz) is the primary engine with pypdf as fallback.

When to use

• User provides a .pdf file path and asks to read or extract its contents
• User asks to summarize a PDF
• User asks what a PDF document contains
• Academic papers, reports, or any PDF documents

When NOT to use

• URLs pointing to web pages (use defuddle skill instead)
• Image files (use image analysis tools)
• Already-extracted text files

Workflow

1. Extract text

Run the extraction script:

python3 ~/.claude/skills/pdf-reader/scripts/extract_pdf.py <pdf_path>

Options:

• -p, --pages — extract specific pages, e.g. 1-5 or 1,3,7
• -m, --method — force fitz or pypdf (default: auto)
• --json — output structured JSON with metadata
• --metadata-only — print only title, author, page count

2. For large PDFs

Extract in chunks to avoid token overflow:

• First run with --metadata-only to check page count
• Then extract page ranges with -p 1-10, -p 11-20, etc.

3. For scanned PDFs (image-based)

If extracted text is empty or garbled, the PDF is likely scanned. Inform the user that OCR is needed. Consider suggesting marker or ocrmypdf as next steps.

Quality notes

• PyMuPDF preserves font size and bold formatting as markdown headers and bold text
• pypdf is the fallback and produces plain text only
• Both handle multi-column layouts as single-column output (reading order)
• Tables are extracted as plain text, not as structured tables