paper-to-code
Implement AI/ML research papers from scratch when no official code exists. Use when the user wants to reproduce a paper, implement an algorithm from a PDF, build a model architecture from a research paper, or create working code from academic publications. Handles papers from arXiv, NeurIPS, ICML, ICLR, CVPR, and other venues. Produces UV-managed, GPU-ready Python projects with tests, demos, and documentation.
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About paper-to-code
The 'paper-to-code' skill allows users to implement AI and machine learning research papers from scratch when no official code is available. This skill is designed to assist users who want to reproduce research, implement algorithms from academic papers, or build models and architectures described in publications such as those from arXiv, NeurIPS, ICML, ICLR, and CVPR. The skill provides a streamlined process to convert a research paper into a working Python project with minimal effort, ensuring users can focus on the core logic and implementation instead of the complexities of setup and environment configuration. By automating many of the steps involved in translating research into executable code, this tool helps save time and ensures greater accuracy in reproducing complex AI models and algorithms.
FAQ
How do I use the 'paper-to-code' skill to reproduce a research paper?
You can start by converting the research paper into markdown using the 'markitdown' tool, then use search commands to extract key components like algorithms, equations, and architecture descriptions. Afterward, an implementation checklist is created to document the core algorithms, model components, and hyperparameters before starting the coding process.
Is this tool compatible with any type of research paper?
The 'paper-to-code' skill supports papers from major AI/ML conferences and journals such as arXiv, NeurIPS, ICML, ICLR, CVPR, and similar venues. It is tailored to handle academic publications related to machine learning and AI algorithms.
What are the hardware and software requirements for using 'paper-to-code'?
The tool is designed to produce GPU-ready Python projects, so you will need a machine with GPU support for optimal performance. It also requires Python 3.11 and dependencies like PyTorch, NumPy, and SciPy. You can install the required dependencies with the 'uv' package manager.
Can I use this tool for papers that have no code available?
Yes, this skill is specifically designed to help you implement research papers when no official code exists, making it ideal for reproducing results or building models based on the described algorithms and methods.
What kind of output does 'paper-to-code' generate?
The skill produces UV-managed, production-ready Python projects that include a complete project structure with tests, demos, and documentation. The output also includes pre-configured model components, utility functions, and hyperparameter documentation.
Install paper-to-code
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
aryateja2106/nested-learning