alaliqing/claude-paper

What it solves

Claude Paper transforms the process of reading research papers from a passive activity into an interactive learning environment. It automates the tedious parts of paper analysis—such as extracting text, finding related code repositories, and summarizing complex sections—allowing researchers and students to focus on understanding the core concepts.

How it works

The project acts as a plugin for AI agents (Claude Code, Codex, OpenCode, and DeepSeek Harness). When a user provides a PDF or a URL, the system triggers a workflow that:

1. Parses the PDF: Extracts text, metadata (authors, title), and images.
2. Analyzes Content: Detects the paper's complexity and type to generate adaptive materials like detailed summaries, key insights, and Q&A.
3. Generates Code: Creates clean reference implementations and Jupyter notebooks to demonstrate the paper's theories.
4. Organizes Data: Stores everything in a structured local library (~/claude-papers/).
5. Visualizes: Launches a Nuxt.js-based web viewer with KaTeX support for rendering mathematical equations.

Who it’s for

• Researchers and Students: Who need to digest large volumes of academic papers efficiently.
• AI Developers: Who want to quickly move from a theoretical paper to a working code implementation.
• Academic Learners: Who prefer a structured, interactive study guide over a static PDF.

Highlights

• Cross-Agent Compatibility: Works across multiple AI agent hosts including Claude Code and DeepSeek Harness.
• Adaptive Learning: Generates different materials (READMEs, insights, reflections) based on the paper's difficulty.
• Automated Code Discovery: Automatically identifies and links to GitHub, arXiv, and CodeOcean repositories.
• Integrated Web Viewer: A dedicated local interface for browsing the paper collection and viewing math-heavy content.