paper-finder

name	paper-finder
description	Search for academic papers and their source code repositories using multi-source APIs (arXiv, Semantic Scholar, HuggingFace Papers, GitHub).

Paper Finder Skill

Purpose

Find academic paper metadata and associated source code repositories from multiple data sources. This is the first step in our paper reproduction pipeline.

When to Use

• User asks to "find", "search", or "look up" a paper
• User provides an arXiv ID, DOI, paper title, or URL
• User asks to "reproduce" or "replicate" a paper (run this first to find the paper and code)
• User wants to know if a paper has official source code

How to Use

Step 1: Run the Search Script

Execute the Python script with the user's query:

python skills/paper-finder/scripts/search_paper.py "<query>" --output workspace/paper_info.json

Supported query formats:

• arXiv ID: 1706.03762, 2301.12345
• arXiv URL: https://arxiv.org/abs/1706.03762
• DOI: 10.5555/3295222.3295349
• Paper title: Attention Is All You Need
• PDF path: /path/to/paper.pdf (uses filename as title hint)

Step 2: Read the Results

After execution, read workspace/paper_info.json to get:

{
  "found": true/false,
  "paper": {
    "title": "...",
    "authors": ["..."],
    "arxiv_id": "...",
    "doi": "...",
    "pdf_url": "...",
    "venue": "...",
    "citation_count": 123,
    "journal_url": "https://doi.org/..."
  },
  "code": {
    "found": true/false,
    "repositories": [
      {
        "url": "https://github.com/...",
        "is_official": true/false,
        "confidence": 0.85,
        "source": "hf_papers|abstract_url|github_search",
        "reason": "..."
      }
    ]
  }
}

Step 3: Interpret Results

• is_official: true (confidence ≥ 0.50): Likely the authors' official repository
• source: "abstract_url": GitHub URL was found directly in the paper text — very reliable
• source: "hf_papers": Repository linked by HuggingFace community — reliable
• source: "github_search": Found via GitHub search — verify manually

Step 4: Present Findings to User

Summarize the results in a clear format:

1. Paper title, authors, year
2. PDF download link
3. Code repositories found (sorted by confidence)
4. Whether official code was identified

Dependencies

• Python 3.10+
• requests library (pip install requests)

Optional: GitHub Token

Set GITHUB_TOKEN environment variable for higher GitHub API rate limits (30 req/min vs 10 req/min). Without a token, the tool still works but may hit rate limits during heavy use.

Error Handling

• If Semantic Scholar returns 429 (rate limited), the search continues with other sources
• If no paper is found, suggest the user try a different query format
• If paper is found but no code, inform the user that the paper may not have public code
• Check search_log in the output for detailed step-by-step information

Data Sources

1. arXiv API — Paper metadata and PDF links
2. Semantic Scholar — Journal DOI, citation count, cross-references
3. HuggingFace Papers — Paper → GitHub repo mapping (replaced Papers with Code)
4. GitHub Search — Fallback repository search