PrathamLearnsToCode/paper2code
An agent skill that converts arxiv paper URLs into complete, citation-anchored ML implementations.
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This tool receives an arxiv paper URL and produces a fully structured codebase including model.py, loss.py, train.py, evaluate.py, configs, and a walkthrough notebook. Each generated file is annotated with the specific paper sections it implements, creating a citation-traceable implementation. It automates the translation of research papers into runnable ML code, targeting ML practitioners and researchers who want to reproduce or build on published work.
Frequently asked
- What is PrathamLearnsToCode/paper2code?
- An agent skill that converts arxiv paper URLs into complete, citation-anchored ML implementations.
- Is paper2code open source?
- Yes — PrathamLearnsToCode/paper2code is open source, released under the MIT license.
- What language is paper2code written in?
- PrathamLearnsToCode/paper2code is primarily written in Python.
- How popular is paper2code?
- PrathamLearnsToCode/paper2code has 1.5k stars on GitHub.
- Where can I find paper2code?
- PrathamLearnsToCode/paper2code is on GitHub at https://github.com/PrathamLearnsToCode/paper2code.