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name: paper-reading description: "Paper reading and analysis framework using Moonshot AI (Kimi). Actions: download, analyze, read, summarize, implement papers from arXiv, SIGGRAPH, etc. Features: intelligent paper fetching, AI deep analysis, personalized reading guidance, knowledge internalization, code generation. Supports: PDF parsing, multi-dimensional analysis (summary, innovation, methodology, implementation), structured notes, knowledge graphs, code framework generation."

Paper Reading - Academic Paper Analysis Framework

Complete framework for precision reading, internalization, and implementation of academic papers using Moonshot AI (Kimi).

Prerequisites

Check if Python is installed:

Copypython3 --version || python --version

If Python is not installed, install it based on user's OS:

macOS:

Copybrew install python3

Ubuntu/Debian:

Copysudo apt update && sudo apt install python3

Windows:

Copywinget install Python.Python.3.12

Install required dependencies:

Copypip install requests openai python-dotenv beautifulsoup4 lxml PyPDF2 markdown pyyaml tqdm aiohttp

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How to Use This Skill

When user requests paper reading work (download, analyze, read, summarize, implement), follow this workflow:

Step 1: Analyze User Requirements

Extract key information from user request:

• Paper source: arXiv ID, URL, or local PDF path
• Action type: download, analyze, full (download + analyze + code generation)
• Analysis type: comprehensive, summary, methodology, innovation, implementation
• Reader profile: amateur or professional (affects explanation depth)

Step 2: Execute Paper Processing

Use paper_skill.py to process the paper:

Copypython3 .claude/skills/paper-reading/scripts/paper_skill.py <arxiv_id_or_url> [--action <action>] [--type <type>]

Available actions:

• download - Only download the paper
• analyze - Analyze existing paper
• full - Complete workflow (download + analyze + code generation)

Available analysis types:

• comprehensive - Full analysis (default)
• summary - Brief summary
• methodology - Methodology details
• innovation - Innovation points
• implementation - Implementation guide

Step 3: Access Results

After processing, results are saved in:

• Notes: data/papers/<paper_id>/notes/
• Summaries: data/papers/<paper_id>/summaries/
• Code: data/papers/<paper_id>/code/
• Knowledge Graph: data/papers/<paper_id>/knowledge/

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Search Reference

Supported Paper Sources

Analysis Types

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Example Workflow

User request: "Analyze paper 2301.12345 and generate implementation code"

AI should:

Copy# Execute full workflow
python3 .claude/skills/paper-reading/scripts/paper_skill.py 2301.12345 --action full

Then: Review the generated files and provide summary:

• Notes location
• Summary location
• Code directory
• Key findings

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Tips for Better Results

1. Use full action for complete analysis - Downloads, analyzes, and generates code
2. Specify analysis type - Choose appropriate type for your needs
3. Check generated files - Review notes, summaries, and code structure
4. Iterate on analysis - Run different analysis types for different perspectives
5. Review implementation guide - Check code directory for implementation details

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Configuration

The skill uses environment variables or config.yaml for configuration:

Required:

• MOONSHOT_API_KEY - Your Moonshot AI API key

Optional (in config.yaml):

• moonshot.model - Model to use (moonshot-v1-8k, moonshot-v1-32k, moonshot-v1-128k)
• moonshot.temperature - Temperature parameter (0.0-1.0)
• paper_reading.paper_workspace_dir - Output directory for results

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Common Workflows

Quick Summary

Copypython3 .claude/skills/paper-reading/scripts/paper_skill.py 2301.12345 --action analyze --type summary

Full Analysis with Code

Copypython3 .claude/skills/paper-reading/scripts/paper_skill.py 2301.12345 --action full

Download Only

Copypython3 .claude/skills/paper-reading/scripts/paper_skill.py 2301.12345 --action download

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Output Structure

Copydata/papers/<paper_id>/
├── notes/              # Structured notes
├── summaries/          # Paper summaries
├── code/               # Generated code framework
│   ├── README.md
│   ├── main.py
│   ├── algorithm.py
│   └── implementation_guide.md
└── knowledge/          # Knowledge graph
    └── knowledge_graph.json