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Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 170,000+ scientists worldwide. 158 ready-to-use 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.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
It solves the fragmentation of scientific tooling by providing a standardized, reusable skill library that any AI agent can use, eliminating the need for researchers to manually integrate disparate databases and computational methods.
Deployment reality
The available setup signal starts with: Install via pip: pip install scientific-agent-skills. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build AI research assistants that query genomic databases, predict regulatory sequences, monitor pathogen variants, perform drug-target binding analysis, run molecular dynamics simulations, and execute geospatial or time series forecasting—all within a single agent framework.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
K-Dense-AI/scientific-agent-skills is tracked as a Python project in the Data Tool, AI Agent, Developer Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 33k total stars, with +0 today, +660 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 20 days ago, and the open issue queue is 8, about 0.02% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 3.2k forks and 33k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need Python teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 911. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
33k Stars
Reuse
3.2k Forks
Attention
33k Watchers
Maintenance
active
License
MIT
Open issues
8
Scientific Agent Skills is a collection of 156 ready-to-use scientific and research skills for AI agents, covering domains like bioinformatics, drug discovery, proteomics, and more. It works with any AI agent supporting the open Agent Skills standard, transforming agents into research assistants capable of complex multi-step scientific workflows.
Key Features
- 156 ready-to-use skills covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, and more. - Works with Cursor, Claude Code, Codex, Google Antigravity, and any AI agent supporting the open Agent Skills standard. - Integrates with 100+ scientific databases and includes skills for bioinformatics, cheminformatics, proteomics, clinical research, and more.
Data Tool
Databases, data pipelines, ETL, analytics, and vector search
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
Developer Tool
Tools that improve coding, testing, build, and local workflow
Install via pip: pip install scientific-agent-skills80
Health Score
Active
Commit Activity
Oct 19, 2025
Created
Aug 3, 2026
Last push
+200
Today's growth
+911
7-day growth
+911
30-day growth
Forks
Open
Watchers
K-Dense-AI
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✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It solves the fragmentation of scientific tooling by providing a standardized, reusable skill library that any AI agent can use, eliminating the need for researchers to manually integrate disparate databases and computational methods.
Capabilities
Developers can build AI research assistants that query genomic databases, predict regulatory sequences, monitor pathogen variants, perform drug-target binding analysis, run molecular dynamics simulations, and execute geospatial or time series forecasting—all within a single agent framework.
Bottom Line
This project is ideal for scientists and developers who want to rapidly prototype AI-driven research workflows without reinventing the wheel. Those needing highly specialized, non-standard tools may find the library limiting. The key trade-off is breadth versus depth: 156 skills cover many domains but each may lack the depth of domain-specific tools.