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Process PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.

⭐11.9kstars🔀3.1kforks📁View on GitHub🕐Updated Dec 30, 2025
[Document Processing]

When & Why to Use This Skill

This Claude skill provides a comprehensive suite of tools for professional PDF manipulation, enabling users to extract text, generate documents from Markdown or HTML, and perform advanced operations like merging and splitting. It simplifies complex document workflows by leveraging powerful libraries such as PyMuPDF, ReportLab, and Pandoc, making it an essential tool for automated document management, data extraction, and programmatic PDF creation.

Use Cases

  • 1. Automated Report Generation: Seamlessly convert Markdown, HTML, or raw text into professional PDF reports with custom styling and layouts.
  • 2. Data Extraction and Analysis: Efficiently extract structured text and metadata from PDF files to automate data entry, research, or content auditing workflows.
  • 3. Document Assembly and Organization: Merge multiple PDF files into a single cohesive document or split large files into individual pages for easier distribution and organization.
  • 4. Programmatic Document Conversion: Transform web content or dynamic data into high-quality PDF documents for archiving, offline reading, or official distribution.
namepdf
descriptionProcess PDF files - extract text, create PDFs, merge documents. Use when user asks to read PDF, create PDF, or work with PDF files.

PDF Processing Skill

You now have expertise in PDF manipulation. Follow these workflows:

Reading PDFs

Option 1: Quick text extraction (preferred)

# Using pdftotext (poppler-utils)
pdftotext input.pdf -  # Output to stdout
pdftotext input.pdf output.txt  # Output to file

# If pdftotext not available, try:
python3 -c "
import fitz  # PyMuPDF
doc = fitz.open('input.pdf')
for page in doc:
    print(page.get_text())
"

Option 2: Page-by-page with metadata

import fitz  # pip install pymupdf

doc = fitz.open("input.pdf")
print(f"Pages: {len(doc)}")
print(f"Metadata: {doc.metadata}")

for i, page in enumerate(doc):
    text = page.get_text()
    print(f"--- Page {i+1} ---")
    print(text)

Creating PDFs

Option 1: From Markdown (recommended)

# Using pandoc
pandoc input.md -o output.pdf

# With custom styling
pandoc input.md -o output.pdf --pdf-engine=xelatex -V geometry:margin=1in

Option 2: Programmatically

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("output.pdf", pagesize=letter)
c.drawString(100, 750, "Hello, PDF!")
c.save()

Option 3: From HTML

# Using wkhtmltopdf
wkhtmltopdf input.html output.pdf

# Or with Python
python3 -c "
import pdfkit
pdfkit.from_file('input.html', 'output.pdf')
"

Merging PDFs

import fitz

result = fitz.open()
for pdf_path in ["file1.pdf", "file2.pdf", "file3.pdf"]:
    doc = fitz.open(pdf_path)
    result.insert_pdf(doc)
result.save("merged.pdf")

Splitting PDFs

import fitz

doc = fitz.open("input.pdf")
for i in range(len(doc)):
    single = fitz.open()
    single.insert_pdf(doc, from_page=i, to_page=i)
    single.save(f"page_{i+1}.pdf")

Key Libraries

Task Library Install
Read/Write/Merge PyMuPDF pip install pymupdf
Create from scratch ReportLab pip install reportlab
HTML to PDF pdfkit pip install pdfkit + wkhtmltopdf
Text extraction pdftotext brew install poppler / apt install poppler-utils

Best Practices

  1. Always check if tools are installed before using them
  2. Handle encoding issues - PDFs may contain various character encodings
  3. Large PDFs: Process page by page to avoid memory issues
  4. OCR for scanned PDFs: Use pytesseract if text extraction returns empty