paper-finder
Search for academic papers and their source code repositories using multi-source APIs (arXiv, Semantic Scholar, HuggingFace Papers, GitHub).
ソース情報
- リポジトリ
- orange4664/research-skills
- ソースの最終更新活動
- 2026年3月31日 03:21
- 検出された SKILL.md の言語
- 英語
- スター
- 41
- フォーク
- 1
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- 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:
```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
GitHubで見る