Does it support command-line PDF tools?

Yes, it integrates with industry-standard CLI tools like qpdf, pdftotext, and pdftk for efficient document processing.

Can this skill help with PDF form filling?

Absolutely, it provides specialized guidance for programmatically filling out PDF forms using libraries like pdf-lib or pypdf.

Can I extract data from scanned PDFs?

Yes, the skill includes specific patterns for using pytesseract and pdf2image to perform OCR on scanned document images.

Which Python libraries does this skill support?

This skill provides guidance and implementation patterns for pypdf, pdfplumber, reportlab, and pypdfium2.

PDF Processing Toolkit

Name: PDF Processing Toolkit
Author: rysweet

byrysweet

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内容管理

Manipulates, extracts, and generates PDF documents through a comprehensive suite of programmatic tools and Python libraries.

关于

The PDF Processing skill for Claude Code provides a specialized environment for handling complex document tasks programmatically. It integrates powerful Python libraries like pypdf and pdfplumber alongside robust command-line utilities to enable seamless text and table extraction, document merging, form filling, and metadata management. Whether you are building automated data entry pipelines from scanned files using OCR or generating dynamic business reports, this skill provides the patterns and best practices needed for high-scale PDF automation.

主要功能

16 GitHub stars
Automated table detection and conversion to structured DataFrames
Comprehensive document manipulation including merging, splitting, and rotating
Support for OCR on scanned documents via pytesseract integration
Advanced text and layout extraction from complex PDF documents
Programmatic PDF creation and dynamic report generation using ReportLab

使用场景

Automating data entry by extracting tables from financial statements and invoices
Building document processing pipelines for bulk watermarking and password protection
Generating dynamic PDF reports, certificates, or invoices from backend data

关于

主要功能

16 GitHub stars
Automated table detection and conversion to structured DataFrames
Comprehensive document manipulation including merging, splitting, and rotating
Support for OCR on scanned documents via pytesseract integration
Advanced text and layout extraction from complex PDF documents
Programmatic PDF creation and dynamic report generation using ReportLab

使用场景

Automating data entry by extracting tables from financial statements and invoices
Building document processing pipelines for bulk watermarking and password protection
Generating dynamic PDF reports, certificates, or invoices from backend data