spring-ai-alibaba/examples

Overview

This skill lets you search the arXiv preprint repository for papers across physics, mathematics, computer science, quantitative biology, and related fields. It provides a lightweight Python script and a shell-friendly interface to run queries and receive formatted results. The tool prioritizes relevance and returns titles and abstracts with fast, unauthenticated access.

How this skill works

The script uses the arXiv API via the arxiv Python package to submit queries, retrieve matching preprints, and print each paper's title and abstract. Results are ordered by relevance and separated by blank lines for easy reading or downstream parsing. The script accepts a free-text query and an optional maximum number of papers to return.

When to use it

• When you need preprints or the latest research before journal publication
• To find machine learning or computational-methods papers applied to biology
• When searching for mathematical or statistical methods relevant to a problem
• To compile literature for a review, experiment, or reproducibility check
• When you want quick, unauthenticated access to arXiv search results

Best practices

• Craft concise, specific queries with keywords and phrases (e.g., "deep learning protein structure")
• Limit --max-papers to a reasonable number for faster responses and easier filtering
• Run the script inside a Python virtual environment and install the arxiv package there
• Verify preprints for peer-review status and check final published versions before citing
• Pipe or redirect output to files if you plan to parse results programmatically

Example use cases

• Quickly find recent papers on neural network approaches for protein folding
• Search for new algorithms in genome assembly or single-cell RNA-seq analysis
• Gather candidate papers for a literature review on statistical learning methods
• Monitor arXiv for new uploads in cs.LG, stat.ML, or q-bio categories
• Fetch abstracts to screen relevance before downloading full PDFs