scientific-agent-skills — What is it?

K-Dense-AI/scientific-agent-skills is a comprehensive collection of 135 ready-to-use scientific and research skills for AI agents, enabling complex scientific workflows across various domains.

⭐ 27,052 Stars 🍴 2,795 Forks Python MIT Author: K-Dense-AI
Source: README View on GitHub →

Why it matters

This project is gaining attention due to its broad compatibility with various AI agents, extensive coverage of scientific domains, and the inclusion of a free, open-source AI co-scientist. The unique technical choice of providing curated documentation and examples for each skill stands out.

Source: README

Core Features

135 Scientific and Research Skills

A comprehensive set of skills covering bioinformatics, cheminformatics, proteomics, clinical research, machine learning, materials science, physics, engineering, data analysis, geospatial science, laboratory automation, and scientific communication.

Source: README
Access to 100+ Scientific & Financial Databases

Unified access to 78+ databases and dedicated skills for various platforms, including PubChem, ChEMBL, UniProt, and Hugging Science.

Source: README
70+ Optimized Python Package Skills

Explicitly defined skills for packages like RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, and others, with curated documentation and examples.

Source: README
9 Scientific Integration Skills

Skills for integrating with platforms like Benchling, DNAnexus, LatchBio, and OMERO, providing optimized, pre-documented paths.

Source: README
30+ Analysis & Communication Tools

Tools for literature review, scientific writing, peer review, document processing, and more.

Source: README
10+ Research & Clinical Tools

Tools for hypothesis generation, grant writing, clinical decision support, and regulatory compliance.

Source: README

Architecture

The architecture is modular, with each skill being a separate module. It uses a unified database-lookup skill for accessing multiple databases and leverages various Python packages for scientific computations. The code structure is organized into subdirectories for different skill categories.

Source: Code tree

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) cisco-ai-skill-scannercisco-ai-skill… firecrawl-py python-dotenv 135 Scientific and Research Skills135 Scientific and… Access to 100+ Scientific & Financial DatabasesAccess to 100+ Scie… 70+ Optimized Python Package Skills70+ Optimized Pytho… 9 Scientific Integration Skills9 Scientific Integr… 30+ Analysis & Communication Tools30+ Analysis & Comm… 10+ Research & Clinical Tools10+ Research & Clin… scientific-agent-ski… Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguagePythonFrameworkNot specified
cisco-ai-skill-scannerfirecrawl-pypython-dotenv
Not specified
Source: Dependency files

Quick Start

Install Scientific Agent Skills with a single command: ```bash npx skills add K-Dense-AI/scientific-agent-skills ```
Source: README Installation/Quick Start

Use Cases

This project is for researchers, scientists, engineers, and data analysts who need to automate complex scientific workflows, access a wide range of scientific databases, and integrate with various scientific tools and platforms.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Extensive coverage of scientific domains
  • Strength 2: Comprehensive set of skills and tools
  • Strength 3: Well-documented and easy to integrate

Limitations

  • Limitation 1: May require significant setup and configuration
  • Limitation 2: Some skills may not be up-to-date with the latest scientific advancements
Source: Synthesis of README, code structure and dependencies

Latest Release

v2.38.0 (2026-05-01): Added support for Hugging Science and author information.

Source: GitHub Releases

Verdict

K-Dense-AI/scientific-agent-skills is a valuable resource for researchers and scientists looking to automate complex scientific workflows and leverage a wide range of scientific tools. It is particularly suitable for teams or individuals working in bioinformatics, cheminformatics, and other scientific domains.

Source: Synthesis
Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-05-23 01:02. Quality score: 85/100.

Data sources: README, GitHub API, dependency files