PrathamLearnsToCode

PrathamLearnsToCode / paper2code

Public

Agent skill to turn any arxiv paper into a working implementation

agent claude-code skills
76
10
100% credibility
Found Apr 03, 2026 at 76 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that helps AI coding assistants generate faithful, section-referenced code implementations directly from machine learning research papers.

How It Works

1
📰 Find a research paper

You're excited about a new machine learning idea from a paper and want to try it out yourself.

2
🔧 Add the helper tool

You easily add this special skill to your friendly AI coding buddy so it can understand papers better.

3
💬 Chat with your AI buddy

Open your AI assistant and tell it about the paper by sharing its web address.

4
✨ Magic happens

Your AI reads the paper carefully, spots every detail and question mark, then builds a complete set of files with notes linking back to the exact parts of the paper.

5
📁 Get your ready-to-use project

You receive a neat folder full of code, settings, instructions, and a guide that explains what matches the paper and what needs your touch.

✅ Run and trust your code

Follow the quick start to see it work, check the notes to verify everything lines up with the paper, and start experimenting with confidence.

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AI-Generated Review

What is paper2code?

paper2code is a Python agent skill that transforms any arXiv paper into a runnable repo with citation-anchored code—feed it a URL like `/paper2code 1706.03762` in your Claude agent or GitHub action, and get modular src files, YAML configs, and notebooks. It solves the ML paper vagueness nightmare: buried hyperparameters, contradictory prose, omitted details—by auditing ambiguities upfront and flagging [UNSPECIFIED] choices with alternatives. Output includes REPRODUCTION_NOTES.md tracing every decision back to sections or equations, perfect for verifiable baselines.

Why is it gaining traction?

It beats naive agent github copilot or claude code gen by enforcing citation anchoring and honest uncertainty—no silent inventions, just SPECIFIED/PARTIALLY_SPECIFIED tags plus appendix mining. Developers love the agent skills library integration (npx skills add for vscode, intellij, or cli), turning paper2code automating code generation into a one-command workflow. Early buzz on agent github copilot reddit and agent skills examples shows it hooks repro-focused teams tired of hallucinated "implementations."

Who should use this?

ML researchers reimplementing SOTA for new papers, PhD students verifying baselines like Transformers or DDPM without weeks of digging, and agent skills anthropic/Claude users building agent github repo pipelines. Ideal for teams in agent skills vs mcp debates needing trustworthy paper-to-code for diffusion models, ViTs, or RL algos.

Verdict

Solid starter for paper repros—76 stars, polished docs, two worked examples (Attention, DDPM)—but 1.0% credibility score flags early maturity; lacks broad tests or 100+ examples. Use for prototyping if you're in ML papers daily; contribute worked reviews to boost it.

(198 words)

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