document-processor

Extract content from PDFs and DOCX into Markdown-ready text.

2|Updated Dec 24, 2025
One-click install
npx skills add https://github.com/adnanmueller/am-dev-plugins --skill document-processor-adnanmueller
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: document-processor
Source: https://github.com/adnanmueller/am-dev-plugins/tree/main/plugins/document-processor
Command: npx skills add https://github.com/adnanmueller/am-dev-plugins --skill document-processor-adnanmueller

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, python-docx, pdf2image, pytesseract, Pillow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a robust toolchain to extract content from PDFs and DOCX files, enabling you to convert and integrate documents into note-taking and knowledge bases with consistent formatting.

Core Features & Use Cases

  • Extract text and content from PDFs and DOCX while preserving structure
  • Convert DOCX to Markdown with headings, lists, tables, and emphasis
  • Split large documents into manageable chunks and enable OCR for scanned documents
  • Seamlessly integrate with Obsidian or other markdown note workflows

Quick Start

Process a sample PDF or DOCX to verify extraction and Markdown conversion.

Frequently Asked Questions about document-processor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert PDF and DOCX files to Markdown for Obsidian?▼

To convert PDF and DOCX files to Markdown for Obsidian, this tool extracts text and content while preserving structural elements like headings, lists, and tables. It outputs clean Markdown-ready text for seamless integration into your note-taking workflows.

Can I extract text from scanned PDFs using OCR?▼

Yes, you can extract text from scanned PDFs using OCR. The tool leverages Tesseract OCR and pdf2image to process scanned documents, converting image-based pages into clean, Markdown-ready text for archival workflows.

Does this approach preserve headings and tables when converting DOCX to Markdown?▼

Yes, converting DOCX to Markdown preserves headings, lists, tables, and emphasis. The extraction process uses python-docx to maintain original document structure, ensuring structured Markdown output for documentation and research.

What is the best way to split large PDFs into manageable Markdown chunks?▼

The best way to split large PDFs into manageable chunks is using the built-in page-splitting feature. It divides large documents during extraction, outputting structured Markdown text that is easier to manage in note-taking bases.

Do I need Python libraries installed to process documents into Markdown?▼

Yes, you need Python libraries installed to process documents into Markdown. The toolchain relies on pypdf, python-docx, pytesseract, pdf2image, and Pillow to extract content and format it into Markdown-ready text.