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 a 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 structured results including paper metadata and code repositories. ### 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 clearly: paper title, authors, year, PDF link, code repos (sorted by confidence). ## Dependencies - Python 3.10+ - `requests` library (`pip install requests`) ## Optional: GitHub Token Set `GITHUB_TOKEN` environment variable for higher GitHub API rate limits. ## Error Handling - If Semantic Scholar returns 429, the search continues with other sources - If no paper is found, suggest the user try a different query format - Check `search_log` in output JSON for detailed step-by-step information