How do I install the Academic Research skill?

Run npx skillmds add majiayu000/academic-research-4 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Academic Research safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Academic Research?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Academic Research free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Academic Research?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Academic Research by majiayu000 — Agent Skill

Name: Academic Research
Rating: 4.2 (5 reviews)
Author: majiayu000

Academic Research

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.

majiayu000 5f81d08 2 files · 3.0 KB Updated Sep 9, 2026 567 repo stars

File contents

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

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

---name: academic-research-4description: 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 ```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".

Frequently asked questions