Paper code finder

Skill jpmsilva1/ai-research-ecosystem/skills/paper-code-finder

A complete ecosystem for AI-assisted academic research. Features an orchestrated pipeline of 130+ ML skills, persistent state memory, and extreme token efficiency for large codebases.

Install

npx -y skills add jpmsilva1/ai-research-ecosystem --skill paper-code-finder

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Find source code implementations and GitHub repositories for academic AI/ML research papers. Use this skill whenever a user asks for the code, repository, huggingface, or implementation of a paper. Ensure you trigger this even if they just say "is there code for this?" while looking at a paper.

SKILL.md

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Paper-Code-Finder: Code Discovery for Academic Papers

This skill locates the official or unofficial source code implementations for academic papers, utilizing highly efficient search strategies specifically optimized for the AI/ML ecosystem.

1. Input Processing & Entity Extraction

First, analyze the user's input to extract key metadata:

Title, Authors, Affiliations: Extract these from the prompt, PDF, arXiv link, or DOI.
ML Framework Semantic Extraction: If a PDF or abstract is provided, scan the "Experiments" or "Implementation Details" section for keywords like PyTorch, JAX, Flax, TensorFlow, or Diffusers. Use this to narrow down your search queries.

2. Search Strategy (The Waterfall)

Execute a structured, sequential search to find the code. Progress through these steps sequentially until you find a match. For highest precision, explicitly use the exa-search or tavily-web skills/tools if they are available to you.

Phase 1: The Fast-Path (PapersWithCode)

Search "[Title] paperswithcode". This is the most reliable database.

Phase 2: The Hugging Face & Mega-Repo Hunter

Search "[Title] site:huggingface.co/papers" and "[Title] site:huggingface.co".
If the paper is about foundational models, search for pull requests in mega-repos: "[Title] huggingface/transformers github" or "[Title] huggingface/diffusers github".

Phase 3: The Deep-Path (Author Profile Hunting)

If the above fail, the repo likely has an obscure name.

Direct GitHub: Search "[Title] [Framework] site:github.com".
Find the Authors: Identify the First Author and the Last Author (Principal Investigator).
Find their GitHub: Search "[Author Name] [University/Affiliation] site:github.com".
Inspect the Profile: Look at their pinned repositories or search within their GitHub profile for repos created around the paper's publication year.
Personal Websites: Search for the author's academic homepage (e.g., "[Author Name] [University] homepage").

3. Verification & Classification

When repositories are found, evaluate them:

Official Implementation: Owned by the paper's authors, or explicitly linked inside the paper text/author's website/PapersWithCode.
Unofficial Implementation: A community reproduction. Check the stars and recent activity to gauge quality.

4. Expected Output Format

Return a concise, direct answer to the user. Do not write a long essay. Always include the framework if you discovered it.

If Official Code is Found:

✅ Official Implementation Found: Repository Name (Implemented in [Framework])

If Only Unofficial Code is Found:

⚠️ No Official Code Found, but community implementations exist:

Repo Name (⭐ XXX) - Unofficial [Framework] implementation.

If No Code Exists:

❌ No Code Found. I searched PapersWithCode, Hugging Face, direct GitHub, and the authors' profiles ([Author 1], [Author 2]), but could not find any public implementations.