{"repo":"PrathamLearnsToCode/paper2code","free":true,"listed":false,"github":"https://github.com/PrathamLearnsToCode/paper2code","clone":"git clone https://github.com/PrathamLearnsToCode/paper2code.git","description":"Agent skill to turn any arxiv paper into a working implementation","language":"Python","stars":1497,"topics":["agent","claude-code","skills"],"license":"MIT","category":"ai-agents","readme_excerpt":"paper2code arxiv URL in → citation-anchored implementation out [placeholder: animated GIF showing the full pipeline — paper fetch → parsing → ambiguity audit → code generation → walkthrough notebook] --- Why this exists The problem: ML papers are vague. Critical hyperparameters are buried in appendices or omitted entirely. Prose contradicts equations. \"Standard settings\" refers to nothing specific. When you implement a paper, you spend more time detective-working than coding. What LLMs get wrong: Naive code generation fills in every gap silently and confidently. You get something that runs but doesn't match the paper. Worse, you can't tell which parts are from the paper and which were invented by the model. What paper2code does differently: 1. Citation anchoring — every line of generated code references the exact paper section and equation it implements ( §3.2, Eq. 4 ) 2. Ambiguity auditing — before writing a single line of code, every implementation choice is classified as SPECIFIED , PARTIALLY SPECIFIED , or UNSPECIFIED 3. Honest uncertainty — unspecified choices are flagged with [UNSPECIFIED] comments at the exact line where the choice is made, with common alternatives listed 4. Appendix mining — appendices, footnotes, and figure captions are treated as first-class sources, not ignored The result: code you can trust because you can verify every decision against the paper. --- Install You'll be prompted to: 1. Select agents — pick the coding agents you want to use this skil","default_branch":null,"files":null,"tree":[],"storefront":"/r/PrathamLearnsToCode","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/PrathamLearnsToCode/paper2code/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}