Is it possible to extract PDF tables into structured data?

Yes, by using pdfplumber in combination with pandas, you can extract tables and convert them directly into DataFrames or Excel files.

Can this skill handle scanned PDF documents?

Yes, it includes guidance for using OCR libraries like pytesseract and pdf2image to extract text from scanned files and images.

Can I use this skill to secure or decrypt PDF files?

Absolutely. It provides implementation patterns for adding password protection (encryption) and decrypting files using pypdf and qpdf.

Which Python libraries are used for PDF creation?

The skill primarily utilizes reportlab for creating professional-grade PDFs with custom layouts, graphics, and multi-page support.

PDF Processing & Automation

Name: PDF Processing & Automation
Author: ttmouse

byttmouse

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Content Management

Manages complex PDF workflows including text extraction, table parsing, document creation, and form automation.

About

This skill provides a comprehensive toolkit for programmatic PDF manipulation using industry-standard Python libraries and command-line utilities. It enables Claude to perform high-fidelity text extraction, structured table parsing with layout preservation, and the generation of professional reports from scratch. Beyond simple reading, it covers advanced operations such as OCR for scanned documents, merging and splitting files, page rotation, and securing documents with password encryption, making it an essential resource for automated document processing pipelines.

Key Features

31 GitHub stars
High-fidelity text and table extraction using pdfplumber
OCR capabilities for processing scanned documents and images
Security features for password protection and metadata management
Document manipulation including merging, splitting, and rotating pages
Dynamic PDF generation and report building with reportlab

Use Cases

Generating dynamic invoices, certificates, or reports from application data
Batch processing and merging disparate document sets for archival purposes
Automating the extraction of data from financial statements into structured formats

About

Key Features

31 GitHub stars
High-fidelity text and table extraction using pdfplumber
OCR capabilities for processing scanned documents and images
Security features for password protection and metadata management
Document manipulation including merging, splitting, and rotating pages
Dynamic PDF generation and report building with reportlab

Use Cases

Generating dynamic invoices, certificates, or reports from application data
Batch processing and merging disparate document sets for archival purposes
Automating the extraction of data from financial statements into structured formats