simple-pdf-skill - SKILL.md Agent Skill

name: simple-pdf-skill description: PDF processing skill for creating, editing, extracting, and merging PDFs using Python libraries.

Use when:

Creating new PDFs from data (reports, invoices, receipts)
Editing existing PDFs (adding highlights, annotations, watermarks, images)
Extracting text, tables, or images from PDFs
Merging, splitting, or manipulating PDF pages
Adding Chinese text to PDFs (requires font registration)
Batch processing PDF documents
Converting PDFs to images
Password protection or encryption

NOT suitable for: - Complex HTML/CSS to PDF conversion (use WeasyPrint or pdfkit) - Rich media/interactive PDFs (use commercial libraries) - High-performance vector graphics

Simple PDF Skill

Quick guide for PDF processing using Python libraries.

Library Selection Guide

Choose the right library based on your task:

Task	Library	Guide
Create new PDFs	reportlab	reportlab-guide.md
Edit existing PDFs	PyMuPDF (fitz)	pymupdf-guide.md
Extract text/tables	pdfplumber or PyMuPDF	pdfplumber-guide.md
Merge/split PDFs	PyMuPDF or pypdf	pymupdf-guide.md
Add annotations	PyMuPDF	pymupdf-guide.md
Extract images	PyMuPDF	pymupdf-guide.md
Render to images	pypdfium2	pypdfium2-guide.md
Password protection	pypdf	pypdf-guide.md
Generate charts	matplotlib + reportlab	chart-guide.md

Quick Start Workflow

1. Identify the Task Type

Creating PDFs:

Use reportlab for new documents from scratch
See reportlab-guide.md for complete API

Editing Existing PDFs:

Use PyMuPDF (fitz) for any modifications
Common edits: highlights, annotations, watermarks, merging, splitting
See pymupdf-guide.md

Extracting Content:

Text extraction: pdfplumber or PyMuPDF
Table extraction: pdfplumber (better for tables)
Image extraction: PyMuPDF
See pdfplumber-guide.md or pymupdf-guide.md

2. Special Considerations

Chinese Text Support:

CRITICAL: Default fonts do not support Chinese
Must register Chinese font before use in reportlab
See reportlab-guide.md → Chinese Font Support section
Recommended fonts: WQY Microhei (4.4MB), Noto Sans SC (15MB)

Performance:

For large PDFs, process in chunks
Use fitz (PyMuPDF) for best performance on editing tasks
Use pdfplumber for reliable text extraction

3. Implementation Reference

For implementation patterns and examples:

Code patterns: PATTERNS.md
Complete examples: EXAMPLES.md
Real-world scenarios: SCENARIOS.md
Workflow details: WORKFLOWS.md

Installation

Install required libraries:

pip install reportlab
pip install pymupdf
pip install pdfplumber
pip install pypdf
pip install pypdfium2
pip install fonttools  # For TTF font extraction

For advanced features (OCR, CLI tools):

# For OCR (scanned PDFs)
pip install pytesseract pdf2image
sudo apt-get install tesseract-ocr

# For command-line tools
sudo apt-get install poppler-utils
sudo apt-get install qpdf

Key Rules

reportlab: Canvas coordinates (0,0 at bottom-left), use Pt() for font sizes, Inch() for positioning
PyMuPDF: Uses RGB tuples (0-1 range), not 0-255
Always: Call save() to finalize documents, close documents to free resources
Chinese fonts: ALWAYS register Chinese fonts before using Chinese text in reportlab
Large PDFs: Process in chunks to avoid memory issues
Encrypted PDFs: Handle gracefully with proper password management