This project addresses the common challenge of implementing machine learning papers, where crucial details like hyperparameters are often vaguely specified or omitted, leading to significant detective work for researchers and engineers. It provides an agent skill that transforms an arXiv paper URL into a citation-anchored, verifiable code implementation, saving users time and increasing confidence in their reproductions. The target audience includes ML practitioners who need to quickly and accurately implement research papers.

How It Works

The core innovation lies in "citation anchoring" and "ambiguity auditing." Every line of generated code is linked to the specific section or equation in the source paper it implements. Before generating code, the system audits implementation choices, classifying them as SPECIFIED, PARTIALLY_SPECIFIED, or UNSPECIFIED. Unlike naive code generation, it explicitly flags unspecified choices with comments and lists common alternatives, ensuring transparency and preventing silent assumptions. Appendices, footnotes, and figure captions are treated as primary sources.

Quick Start & Requirements

• Primary install: Use npx skills add PrathamLearnsToCode/paper2code/skills/paper2code.
• Prerequisites: Requires selecting coding agents (e.g., Claude Code), choosing a scope (Global or project-level), and a method (Symlink or copy).
• Usage: After installation, invoke the skill via your agent: claude (or preferred agent) followed by /paper2code <arxiv_url> [--framework <framework>] [--mode <mode>].
• Links: No direct links to official quick-start guides, demos, or extensive documentation beyond the README are provided.

Highlighted Details

• Citation Anchoring: Code decisions are explicitly linked to paper sections (e.g., §3.2 — "We apply layer normalization...").
• Ambiguity Flagging: Uses tags like [UNSPECIFIED], [PARTIALLY_SPECIFIED], and [ASSUMPTION] with explanations and alternative suggestions.
• Structured Output: Generates a project directory including README.md, REPRODUCTION_NOTES.md (ambiguity audit), requirements.txt, source code (src/), configuration files (configs/base.yaml), and a pedagogical walkthrough.ipynb.
• Key Files: model.py maps classes to paper sections, REPRODUCTION_NOTES.md details ambiguity, base.yaml centralizes cited hyperparameters, and walkthrough.ipynb offers runnable sanity checks.

Maintenance & Community

The provided README does not contain specific details regarding notable contributors, sponsorships, community channels (like Discord or Slack), or a public roadmap.

Licensing & Compatibility

The license type is not explicitly stated in the README, which is a critical omission for due diligence. Compatibility for commercial use or closed-source linking cannot be determined without a specified license.

Limitations & Caveats

This tool does not guarantee implementation correctness; it faithfully translates the paper, meaning errors or vagueness in the paper will be reflected in the code. It will not invent details or hyperparameters, instead flagging them as [UNSPECIFIED] with common alternatives. The tool does not handle dataset downloads, training infrastructure setup (e.g., distributed training, experiment tracking), or the implementation of baseline methods; it focuses solely on the core contribution described in the paper.