Agent skill · Databases

openalex-database

Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill openalex-database --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 5
SKILL.md size: 12 KB
Bundled scripts: yes
Path: skills/openalex-database/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# OpenAlex Database ## Overview OpenAlex is a comprehensive open catalog of 240M+ scholarly works, authors, institutions, topics, sources, publishers, and funders. This skill provides tools and workflows for querying the OpenAlex API to search literature, analyze research output, track citations, and conduct bibliometric studies. ## Quick Start ### Basic Setup Always initialize the client with an email address to access the polite pool (10x rate limit boost): ```python from scripts.openalex_client import OpenAlexClient client = OpenAlexClient(email="your-email@example.edu") ``` ### Installation Requirements Install required package using uv: ```bash uv pip install requests ``` No API key required - OpenAlex is completely open. ## Core Capabilities ### 1. Search for Papers **Use for**: Finding papers by title, abstract, or topic ```python # Simple search results = client.search_works( search="machine learning", per_page=100 ) # Search with filters results = client.search_works( search="CRISPR gene editing", filter_params={ "publication_year": ">2020", "is_oa": "true" }, sort="cited_by_count:desc" ) ``` ### 2. Find Works by Author **Use for**: Getting all publications by a specific r

What's inside
Steps it walks through
  1. Overview
  2. Quick Start
  3. Basic Setup
  4. Installation Requirements
  5. Core Capabilities
  6. 1. Search for Papers
  7. 2. Find Works by Author
  8. 3. Find Works from Institution
  9. 4. Highly Cited Papers
  10. 5. Open Access Papers
  11. 6. Publication Trends Analysis
  12. 7. Research Output Analysis
  13. 8. Batch Lookups
  14. 9. Random Sampling
Ships with 4 files
  • references/api_guide.md
  • references/common_queries.md
  • scripts/openalex_client.py
  • scripts/query_helpers.py
Commands it runs
uv pip install requests
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the openalex-database skill do?

Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill openalex-database --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going