Academic Search

Ideal Use Cases

How to Use

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

Basic Usage

Note: Always use the absolute path from your skills directory.

If running from a virtual environment:

.venv/bin/python [YOUR_SKILLS_DIR]/academic-search/arxiv_search.py "your search query"

Or for system Python:

python3 [YOUR_SKILLS_DIR]/academic-search/arxiv_search.py "your search query"

Replace [YOUR_SKILLS_DIR] with the absolute skills directory path from your system prompt.

Command-Line Arguments

Examples

Search for transformer architecture papers:

python3 arxiv_search.py "attention is all you need transformer" --max-papers 5

Search for reinforcement learning papers:

python3 arxiv_search.py "deep reinforcement learning continuous control" --max-papers 10

Search for LLM papers with JSON output:

python3 arxiv_search.py "large language model reasoning" --output-format json

Search for specific author or topic:

python3 arxiv_search.py "author:Hinton deep learning"

Search in specific arXiv categories:

python3 arxiv_search.py "cat:cs.LG neural network pruning"

Step-by-Step Workflow

1. Formulate Your Query

• Use specific, technical terms (e.g., "convolutional neural network image segmentation" not "AI for pictures")
• Include key authors if known: author:Bengio
• Specify arXiv categories for focused results: cat:cs.CL (Computation and Language)
• Combine terms for intersection: "graph neural network" AND "molecular property"

2. Execute the Search

python3 [SKILLS_DIR]/academic-search/arxiv_search.py "your refined query" --max-papers 10

3. Review Results

The output includes:

• Title: Full paper title
• Authors: List of paper authors
• Published: Publication date
• arXiv ID: Unique identifier (useful for citing)
• URL: Direct link to the paper
• Summary: Abstract text

4. Iterate if Needed

• Too many irrelevant results? Add more specific terms or use category filters
• Too few results? Broaden the query or remove restrictive terms
• Looking for recent work? arXiv sorts by relevance by default

5. Save and Synthesize

Save relevant findings to your research workspace for later synthesis:

research_workspace/
  papers/
    topic_findings.md

Output Formats

Text Format (Default)

================================================================================
Title: Attention Is All You Need
Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, ...
Published: 2017-06-12
arXiv ID: 1706.03762
URL: https://arxiv.org/abs/1706.03762
--------------------------------------------------------------------------------
Summary: The dominant sequence transduction models are based on complex
recurrent or convolutional neural networks...
================================================================================

JSON Format

{
  "query": "transformer attention",
  "total_results": 5,
  "papers": [
    {
      "title": "Attention Is All You Need",
      "authors": ["Ashish Vaswani", "Noam Shazeer", ...],
      "published": "2017-06-12",
      "arxiv_id": "1706.03762",
      "url": "https://arxiv.org/abs/1706.03762",
      "summary": "The dominant sequence transduction models..."
    }
  ]
}

Markdown Format

## Attention Is All You Need

**Authors:** Ashish Vaswani, Noam Shazeer, ...
**Published:** 2017-06-12
**arXiv ID:** [1706.03762](https://arxiv.org/abs/1706.03762)

### Abstract
The dominant sequence transduction models are based on complex...

arXiv Category Reference

Common categories for AI/ML research:

Best Practices

Query Construction

1. Be specific: "graph attention network node classification" > "graph neural network"
2. Use quotation marks: For exact phrases: "self-supervised learning"
3. Combine operators: cat:cs.CV AND "object detection" AND 2023
4. Include variations: Search for both "LLM" and "large language model"

Research Workflow Integration

1. Start broad, then narrow: Begin with general queries, refine based on initial results
2. Track paper IDs: Save arXiv IDs for citing and revisiting
3. Check references: Seminal papers often cite foundational work
4. Note publication dates: Preprints may be superseded by updated versions

Limitations to Consider

• Preprint status: Papers may not be peer-reviewed
• Version updates: Check for newer versions (v2, v3, etc.)
• Coverage gaps: Not all fields are well-represented on arXiv
• Rate limiting: Avoid excessive rapid queries

Dependencies

This skill requires the arxiv Python package:

# Virtual environment (recommended)
.venv/bin/python -m pip install arxiv

# System-wide
python3 -m pip install arxiv

The script will detect if the package is missing and display installation instructions.

Troubleshooting

"Error: arxiv package not installed"

Install the arxiv package as shown in Dependencies section.

No results returned

• Try broader search terms
• Remove category restrictions
• Check for typos in technical terms

Rate limiting errors

• Wait a few seconds between queries
• Reduce --max-papers value

Connection errors

• Check internet connectivity
• arXiv API may have temporary outages

Integration with Research Workflow

This skill works well with the web-research skill for comprehensive research:

1. Use academic-search for foundational/theoretical papers
2. Use web-research for current implementations, tutorials, and practical guides
3. Synthesize findings from both sources in your research report

Notes

• arXiv is particularly strong for:
  • Computer Science (cs.*)
  • Physics (physics., hep-, cond-mat.*)
  • Mathematics (math.*)
  • Quantitative Biology (q-bio.*)
  • Statistics (stat.*)
• Results are sorted by relevance by default
• The arXiv API is free and requires no authentication
• Consider checking cited papers for deeper understanding