arxiv-search

GitHub

提供arXiv学术预印本搜索能力,支持物理、计算机等领域文献检索。适用于查找最新研究论文、机器学习及生物信息学相关资源,无需API密钥,直接返回格式化结果。

skills_all/arxiv-search/SKILL.md Microck/ordinary-claude-skills

Trigger Scenarios

需要查找最新学术论文或预印本 搜索特定领域的科研文献如机器学习和生物信息学

Install

npx skills add Microck/ordinary-claude-skills --skill arxiv-search -g -y
More Options

Non-standard path

npx skills add https://github.com/Microck/ordinary-claude-skills/tree/main/skills_all/arxiv-search -g -y

Use without installing

npx skills use Microck/ordinary-claude-skills@arxiv-search

指定 Agent (Claude Code)

npx skills add Microck/ordinary-claude-skills --skill arxiv-search -a claude-code -g -y

安装 repo 全部 skill

npx skills add Microck/ordinary-claude-skills --all -g -y

预览 repo 内 skill

npx skills add Microck/ordinary-claude-skills --list

SKILL.md

Frontmatter
{
    "name": "arxiv-search",
    "description": "Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields"
}

arXiv Search Skill

This skill provides access to arXiv, a free distribution service and open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics.

When to Use This Skill

Use this skill when you need to:

  • Find preprints and recent research papers before journal publication
  • Search for papers in computational biology, bioinformatics, or systems biology
  • Access mathematical or statistical methods papers relevant to biology
  • Find machine learning papers applied to biological problems
  • Get the latest research that may not yet be in PubMed

How to Use

The skill provides a Python script that searches arXiv and returns formatted results.

Basic Usage

Note: Always use the absolute path from your skills directory (shown in the system prompt above).

If running deepagents from a virtual environment:

.venv/bin/python [YOUR_SKILLS_DIR]/arxiv-search/arxiv_search.py "your search query" [--max-papers N]

Or for system Python:

python3 [YOUR_SKILLS_DIR]/arxiv-search/arxiv_search.py "your search query" [--max-papers N]

Replace [YOUR_SKILLS_DIR] with the absolute skills directory path from your system prompt (e.g., ~/.deepagents/agent/skills or the full absolute path).

Arguments:

  • query (required): The search query string (e.g., "neural networks protein structure", "single cell RNA-seq")
  • --max-papers (optional): Maximum number of papers to retrieve (default: 10)

Examples

Search for machine learning papers:

.venv/bin/python ~/.deepagents/agent/skills/arxiv-search/arxiv_search.py "deep learning drug discovery" --max-papers 5

Search for computational biology papers:

.venv/bin/python ~/.deepagents/agent/skills/arxiv-search/arxiv_search.py "protein folding prediction"

Search for bioinformatics methods:

.venv/bin/python ~/.deepagents/agent/skills/arxiv-search/arxiv_search.py "genome assembly algorithms"

Output Format

The script returns formatted results with:

  • Title: Paper title
  • Summary: Abstract/summary text

Each paper is separated by blank lines for readability.

Features

  • Relevance sorting: Results ordered by relevance to query
  • Fast retrieval: Direct API access with no authentication required
  • Simple interface: Clean, easy-to-parse output
  • No API key required: Free access to arXiv database

Dependencies

This skill requires the arxiv Python package. The script will detect if it's missing and show an error.

If you see "Error: arxiv package not installed":

If running deepagents from a virtual environment (recommended), use the venv's Python:

.venv/bin/python -m pip install arxiv

Or for system-wide install:

python3 -m pip install arxiv

The package is not included in deepagents by default since it's skill-specific. Install it on-demand when first using this skill.

Notes

  • arXiv is particularly strong for:
    • Computer science (cs.LG, cs.AI, cs.CV)
    • Quantitative biology (q-bio)
    • Statistics (stat.ML)
    • Physics and mathematics
  • Papers are preprints and may not be peer-reviewed
  • Results include both recent uploads and older papers
  • Best for computational/theoretical work in biology

Version History

  • 1056d29 Current 2026-07-24 18:11

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Metadata

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Version
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Indexed
2026-07-24 18:11

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