paper-finder

# 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: ```bash 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: ```json { "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