Agent Skills
› NeverSight/learn-skills.dev
› arxiv-automation
arxiv-automation
GitHub自动化搜索、监控和下载arXiv学术论文，支持按主题或作者查询，提取并摘要摘要，辅助科研工作流程。
Trigger Scenarios
需要查找特定领域最新论文
监控指定分类下的新提交
批量下载论文PDF进行分析
Install
npx skills add NeverSight/learn-skills.dev --skill arxiv-automation -g -y
SKILL.md
Frontmatter
{
"name": "arxiv-automation",
"description": "Search and monitor arXiv papers. Query by topic, author, or category. Track new papers, download PDFs, and summarize abstracts for research workflows."
}
arXiv Automation
Search, monitor, and analyze academic papers from arXiv.
Capabilities
- Search papers by keyword, author, category
- Monitor new submissions in specific categories
- Download PDFs for analysis
- Extract and summarize abstracts
- Track citation-worthy papers
Usage
Search Papers (arXiv API)
import urllib.request, urllib.parse, xml.etree.ElementTree as ET
def search_arxiv(query, max_results=10):
base_url = "http://export.arxiv.org/api/query?"
params = urllib.parse.urlencode({
"search_query": query,
"start": 0,
"max_results": max_results,
"sortBy": "submittedDate",
"sortOrder": "descending"
})
url = base_url + params
response = urllib.request.urlopen(url).read()
root = ET.fromstring(response)
ns = {"atom": "http://www.w3.org/2005/Atom"}
papers = []
for entry in root.findall("atom:entry", ns):
papers.append({
"title": entry.find("atom:title", ns).text.strip(),
"summary": entry.find("atom:summary", ns).text.strip()[:200],
"link": entry.find("atom:id", ns).text,
"published": entry.find("atom:published", ns).text,
"authors": [a.find("atom:name", ns).text for a in entry.findall("atom:author", ns)]
})
return papers
# Example: search for LLM agent papers
papers = search_arxiv("all:LLM AND all:agent", max_results=5)
for p in papers:
print(f"{p['title']}\n {p['link']}\n {', '.join(p['authors'][:3])}\n")
Monitor Categories
Common CS categories:
| Category | Description |
|---|---|
| cs.AI | Artificial Intelligence |
| cs.CL | Computation and Language (NLP) |
| cs.LG | Machine Learning |
| cs.CV | Computer Vision |
| cs.SE | Software Engineering |
RSS feeds: http://arxiv.org/rss/{category} (e.g., http://arxiv.org/rss/cs.AI)
Download PDF
# arXiv ID format: 2401.12345
arxiv_id = "2401.12345"
pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf"
Rate Limits
- arXiv API: max 1 request per 3 seconds
- Be respectful of arXiv's resources
- Use RSS feeds for monitoring (less load than API queries)
Integration
Combine with pdf skill for PDF text extraction and analysis.
Combine with rss-automation for periodic monitoring of new papers.
Version History
- e0220ca Current 2026-07-05 21:49
