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.
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---
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](file:///C:/Users/Joshua/.gemini/antigravity/brain/126df87b-03b8-439b-8a30-d59c16e53143/skills/academic-research/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
```bash
# 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".