agentsclimarketplace

Academic research

Skill kenantang/codex-and-claude-skills/collected-academic-research-skills/sources/joshuaroll__research-skills/skills/academic-research

Generated by Codex. Read with caution.

Install
npx -y skills add kenantang/codex-and-claude-skills --skill academic-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

2.1 KB, as published. Nobody here has run it

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".

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.