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paper-summary

Extract text from PDF academic papers and generate structured summaries.

5|Updated Mar 23, 2026
Search Tags:#pdf#text analysis#natural language processing#summary generation#academic paper
One-click install
npx skills add https://github.com/scholarclaw/skills --skill paper-summary-scholarclaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paper-summary
Source: https://github.com/scholarclaw/skills/tree/main/skills/paper-summary
Command: npx skills add https://github.com/scholarclaw/skills --skill paper-summary-scholarclaw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyMuPDF==1.26.1, jinja2==3.1.2, and includes scripts (resource) components.

What problem does it solve?

The Skill simplifies the process of creating comprehensive and concise paper summaries, saving valuable time and effort.

Core Features & Use Cases

  • Text Extraction from PDFs: Extracts full text from PDF files efficiently.
  • Structured Summarization: Generates detailed summaries with Key Ideas, Main Contributions, and Experimental Results.
  • Direct Processing: Processes PDFs directly, requiring no additional steps.
  • Integration Capable: Integrate with OpenClaw for seamless operations.
  • Use Case: Use the skill to instantly create a summary of any academic paper, which is especially helpful in quick information retrieval.

Quick Start

Use the paper-summary skill to summarize the content of 'path/to/paper.pdf'.

Frequently Asked Questions about paper-summary

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

FAQPage Schema
How do I generate a summary from an academic paper PDF?▼

To generate a summary from an academic paper PDF, use a natural language processing model that extracts full text directly from the file. It distills the content into Key Ideas, Main Contributions, and Experimental Results for quick retrieval.

What is the best way to extract key ideas and experimental results from research papers?▼

The best way to extract key ideas and experimental results is using structured summarization. It identifies and distills specific academic sections, providing a concise breakdown of main contributions without requiring manual reading.

Can I extract text directly from a PDF for natural language processing?▼

Yes, you can extract text directly from a PDF for natural language processing. The system uses PyMuPDF to efficiently pull full text from PDF files, requiring no additional manual steps before analysis.

Does OpenClaw integrate with academic paper summarization tools?▼

Yes, OpenClaw integrates with academic paper summarization tools. The summarization process is fully compatible with OpenClaw, allowing seamless operations and direct processing of PDF documents within integrated workflows.

How to automate paper summarization for multiple academic PDFs?▼

You automate paper summarization for academic PDFs by processing documents directly through an NLP-based model. It automatically identifies and distills key insights, saving valuable time and effort in information retrieval.

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