Which Python library is best for extracting tables from PDFs?

pdfplumber is the recommended tool within this skill for extracting structured table data and converting it into pandas DataFrames.

Can I generate custom PDFs from scratch?

Absolutely. The skill includes comprehensive guides for using reportlab to create custom PDF layouts, including text, graphics, and branding.

Can this skill help with merging multiple PDF files?

Yes, it provides optimized code snippets using the pypdf library and qpdf command-line tool to merge multiple documents efficiently.

Does it support OCR for scanned documents?

While the focus is on digital PDFs, the skill includes implementation patterns for using pytesseract to perform OCR by first converting PDF pages into images.

Are there CLI tools included for fast processing?

Yes, the skill covers the use of qpdf and poppler-utils (pdftotext) for high-speed document manipulation outside of Python environments.

PDF Manipulation Toolkit

Name: PDF Manipulation Toolkit
Author: skillcreatorai

byskillcreatorai

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178

Productivity & Workflow

Manages, extracts, and generates PDF documents programmatically using Python libraries and high-performance CLI tools.

About

The PDF Manipulation toolkit provides Claude with a comprehensive set of patterns for professional document processing. It streamlines tasks ranging from simple text extraction and document merging to complex table parsing and dynamic PDF generation. By leveraging industry-standard Python libraries like pypdf, pdfplumber, and reportlab, alongside powerful CLI utilities like qpdf, this skill enables AI-driven automation for invoice processing, data extraction, and programmatic document creation. It is an essential resource for developers needing to handle document-based workflows with precision and scale.

Key Features

Optimized command-line workflows for batch processing document sets
Advanced text and table extraction using pdfplumber and pandas
Dynamic PDF generation and canvas-based layout design with reportlab
High-performance PDF merging and splitting via pypdf and qpdf
Support for PDF form filling and programmatic metadata manipulation
178 GitHub stars

Use Cases

Programmatically generating personalized reports, certificates, or invoices from application data
Batch processing document archives for merging, splitting, or OCR preparation
Automating the extraction of structured tabular data from financial statements or invoices

About

Key Features

Optimized command-line workflows for batch processing document sets
Advanced text and table extraction using pdfplumber and pandas
Dynamic PDF generation and canvas-based layout design with reportlab
High-performance PDF merging and splitting via pypdf and qpdf
Support for PDF form filling and programmatic metadata manipulation
178 GitHub stars

Use Cases

Programmatically generating personalized reports, certificates, or invoices from application data
Batch processing document archives for merging, splitting, or OCR preparation
Automating the extraction of structured tabular data from financial statements or invoices