Can this skill handle batch processing of many documents?

Yes, by utilizing the PyMuPDF tool, the skill can process documents at approximately 0.01s per page, making it ideal for large-scale conversions.

What are the local requirements for Docling?

Docling downloads machine learning models (approx. 500MB-1GB) upon the first run, which are then cached locally for all subsequent extractions.

Which tool is best for extracting tables from PDFs?

Docling is the recommended tool for tables, offering 97.9% accuracy and preserving the structural relationship of data for LLM consumption.

Does this skill send my PDFs to an external API?

No, all processing is performed locally on your device using Python libraries like Docling and PyMuPDF, ensuring complete data privacy.

Is the output compatible with LLMs like Claude or GPT?

Absolutely. The skill is specifically designed to output clean Markdown, which is the preferred format for LLMs and RAG systems.

AI-Powered PDF Text Extractor

Name: AI-Powered PDF Text Extractor
Author: WarrenZhu050413

byWarrenZhu050413

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Web Scraping y Recopilación de Datos

Converts PDF documents into LLM-friendly Markdown while preserving complex structures like tables, headers, and lists.

Acerca de

The pdftext skill provides Claude with specialized capabilities to extract high-quality text and structured data from PDF files using a suite of industry-leading tools. It offers a strategic choice between Docling for AI-powered structure preservation (97.9% table accuracy), PyMuPDF for high-speed batch processing, and pdfplumber for maximum fidelity. This skill is particularly useful for developers and researchers building RAG systems or preparing academic papers for LLM consumption, providing optimized Python implementation patterns that run entirely on-device to ensure data privacy and security.

Características Principales

5 GitHub stars
High-speed batch processing at 0.01s/page via PyMuPDF
AI-powered structure preservation with Docling for academic layouts
Optimized Markdown output specifically formatted for RAG systems
100% on-device processing with no external API calls or data leaks
Precision table and list extraction for complex document formats

Casos de Uso

Preparing large-scale PDF datasets for Retrieval-Augmented Generation (RAG) pipelines
Automating the extraction of tabular data from business reports and financial statements
Converting dense academic research papers into structured Markdown for analysis

Acerca de

Características Principales

5 GitHub stars
High-speed batch processing at 0.01s/page via PyMuPDF
AI-powered structure preservation with Docling for academic layouts
Optimized Markdown output specifically formatted for RAG systems
100% on-device processing with no external API calls or data leaks
Precision table and list extraction for complex document formats

Casos de Uso

Preparing large-scale PDF datasets for Retrieval-Augmented Generation (RAG) pipelines
Automating the extraction of tabular data from business reports and financial statements
Converting dense academic research papers into structured Markdown for analysis