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Scientific Agent Skills

k-dense-ai/scientific-agent-skills

Guide AI through research workflows and exploratory data analysis.

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Introduction

Prepared by 今日热榜 from public project documentation

A pack for research and data analysis, with instructions for data inspection, statistics, and scientific tools. It replaces the former claude-scientific-skills repository and is listed once at its canonical address. Analysis skills need Python; others require additional software, database access, or service accounts. External services may have separate charges.

Get started

Setup requirements have not been reviewed yet. Check the project’s setup guide.

Setup and usage guide ↗

Based on public documentation; not individually installed and tested here.

Ways to use it

  • Inspect distributions and missing values
  • Choose analysis steps for a research task
  • Organize results and check data sources

Official project & documentation

View source code ↗Project website ↗Setup and usage guide ↗

The project’s original description

Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.

More projects of this type

These other listed projects can help you compare use cases and setup requirements.

  • Superpowers — Give AI a development workflow for clarifying requirements, planning, coding, and checking results.
  • Matt Pocock Skills — Reproduce a problem and verify its cause before changing code.
  • gstack — Connect planning, code review, and browser checks in a development workflow.

Sources and ranking

This directory lists AI development plugins with public source code and a clear purpose. Browse by task, then open an introduction to learn what a project does, who it is for, and what you need to get started. It covers selected projects, not every AI plugin.

How do the three plugin types differ?

Tool connections (MCP) let AI assistants use external tools such as browsers, code repositories and databases. MCP is the protocol used for these connections.

Reusable skills give a compatible AI assistant instructions and guidance for a particular task.

Editor plugins run inside the software you use to write code, adding AI chat, completions or suggested edits.

How does “Most starred” work?

Projects are sorted by their total GitHub stars. Stars indicate interest; they are not downloads, active users or quality scores. A skill pack counts as one project. Daily star growth is not shown without historical records.

What does “Recently updated” mean?

It uses the most recent code push, which may not be a new release. The 7-day filter checks that project update time; ranking still uses all-time stars. Each introduction shows when its star count was collected. Failed updates keep the last saved data.

Introductions are based on public project documentation. These projects have not each been installed and tested here. Check the project documentation for setup, compatibility and service costs. Refreshing this list only reads saved data; it does not start collection.

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