academic-research

name: academic-research description: A tool for rigorous academic research using Semantic Scholar and ArXiv. Focuses on finding highly-cited papers, retrieving abstracts, and following citation trails to understand the provenance of ideas.

Academic Research Skill

This skill allows you to function as an academic researcher, finding and analyzing scholarly papers with a focus on impact and provenance.

Capabilities

1. Search Papers: Find papers by keyword, ensuring relevance.
2. Analyze Impact: Filter by citation count to identify seminal works.
3. Trace Provenance: (Optional) Find papers that cite a target paper to seeing how the field evolved.
4. Get Details: Retrieve abstracts and direct PDF links.
5. Velocity Metrics: See citations per year to identify "trending" papers.
6. BibTeX Export: Generate citations for your references.

Usage

Run the python script search_papers.py to perform searches.

Arguments

• query (required): The search term.
• --limit (optional): Max results (default 5).
• --year (optional): Year range (e.g., "2023-2025").
• --sort (optional): Sort by "relevance", "citationCount", or "velocity" (new!).
• --open-access (optional): Only return open access papers.
• --format (optional): Output "json" (default) or "bibtex".

Example

# Find "hot" papers on LLMs (high velocity)
python3 search_papers.py "Large Language Models" --sort velocity

# Get BibTeX for a specific search
python3 search_papers.py "Attention is All You Need" --format bibtex

Output Format

The script outputs a JSON object (or JSON-lines) containing:

• title
• authors
• year
• citationCount
• citationsPerYear: Velocity metric.
• tldr: Semantic Scholar's generated summary (if available).
• url
• pdf_url (if available)

Tips for the Agent

• TLDR vs Abstract: The tldr field is often shorter and easier to digest for quick summaries.
• Velocity: A paper from 2024 with 100 citations is often more relevant than a 2010 paper with 500 citations. Use sort="velocity".