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document-speed-reader

Extract and summarize text from PDFs, TXTs, and scanned documents using OCR.

1|Updated Jun 9, 2026
Search Tags:#information retrieval#text extraction#document analysis#PDF processing#document summarization
One-click install
npx skills add https://github.com/lxt070409/hermes-skills --skill document-speed-reader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: document-speed-reader
Source: https://github.com/lxt070409/hermes-skills/tree/main/productivity/document-speed-reader
Command: npx skills add https://github.com/lxt070409/hermes-skills --skill document-speed-reader

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymupdf, Tesseract OCR, python-docx, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of reading and extracting information from documents, offering a fast and efficient way to navigate through content.

Core Features & Use Cases

  • Document Recognition: Automatically recognizes and processes PDFs, TXTs, and scanned documents.
  • Extraction and Summarization: Extracts key information and summarizes documents in a structured format.
  • Use Case: Quickly summarize a 100-page technical document into a concise report, saving time and effort.

Quick Start

To summarize the document, run the command 'document-speed-reader(summarize_document="document.pdf")'.

Frequently Asked Questions about document-speed-reader

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

FAQPage Schema
How do I extract and summarize text from scanned PDF documents?▼

To extract and summarize text from scanned PDFs, this solution uses Tesseract OCR to recognize scanned content and pymupdf to process the PDF file, outputting a concise structured summary of the document's key information.

Can I use this tool to summarize a 100-page technical PDF into a short report?▼

Yes, you can summarize a 100-page technical PDF into a short report by running the summarize command, which extracts key information and formats it into a concise summary to save reading time.

What is the best way to retrieve information from TXT files for research workflows?▼

The best way to retrieve information from TXT files for research workflows is using an automated extraction tool that parses the text and generates a structured summary, streamlining document analysis and information retrieval.

Does document summarization work with python-docx and pymupdf dependencies?▼

Yes, document summarization works utilizing pymupdf for direct PDF text extraction and python-docx for processing text documents, while Tesseract OCR handles any scanned image content within the files.

Why does PDF text extraction fail on scanned administrative documents?▼

PDF text extraction fails on scanned administrative documents because the text is image-based, requiring Tesseract OCR to convert the scanned images into machine-readable text before summarization can occur.

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