One command to turn AI into your personal research scientist!

Great news for researchers! This GitHub tool equips AI with 133 professional research skills, covering bioinformatics, drug discovery, clinical, and multi-omics — boosting efficiency instantly.

How powerful is it?

1. 133 ready-to-use skills covering the full range of biology/chemistry/medicine/materials
2. One-click direct connection to 100+ authoritative databases (PubChem, ChEMBL, UniProt, ClinicalTrials…)
3. Built-in 70+ top-tier Python toolchains (RDKit, Scanpy, PyTorch, scikit-learn, etc.)
4. Fully compatible with Cursor / Claude / Codex, automatic discovery and invocation

10-second quick start: npx skills add K-Dense-AI/scientific-agent-skills

What it can actually do:

1. Drug discovery: target screening → molecular docking → ADMET → one-click report
2. Single-cell: QC → clustering → differential analysis → regulatory network → target mining full workflow
3. Clinical: VCF annotation → pathogenicity interpretation → drug recommendation → clinical trial matching
4. Multi-omics: integrated transcriptomics + proteomics + metabolomics analysis and modeling

Previously, setting up environments, querying databases, and writing code took days. Now everything is handed over to AI, and results come in minutes — paper writing speed maxed out.

GitHub direct link: https://github.com/K-Dense-AI/scientific-agent-skills…

(Already 22k+ stars; highly recommend starring then trying)

K-Dense-AI/scientific-agent-skills

Source: https://github.com/K-Dense-AI/scientific-agent-skills

Scientific Agent Skills

🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills (https://agentskills.io/) standard, not just Claude.

New: K-Dense BYOK (https://github.com/K-Dense-AI/k-dense-byok) — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handling, 100+ scientific databases, and access to all 135 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via Modal (https://modal.com/) for heavy workloads. Get started here. (https://github.com/K-Dense-AI/k-dense-byok)

License: MIT Skills Databases Agent Skills (https://agentskills.io/) Works with X (https://x.com/k_dense_ai) LinkedIn (https://www.linkedin.com/company/k-dense-inc) YouTube (https://www.youtube.com/@K-Dense-Inc)

A comprehensive collection of 135 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open Agent Skills (https://agentskills.io/) standard, created by K-Dense (https://k-dense.ai). Works with Cursor, Claude Code, Codex, and more. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.

These skills enable your AI agent to seamlessly work with specialized scientific libraries, databases, and tools across multiple scientific domains. While the agent can use any Python package or API on its own, these explicitly defined skills provide curated documentation and examples that make it significantly stronger and more reliable for the workflows below:

• 🧬 Bioinformatics & Genomics - Sequence analysis, single-cell RNA-seq, gene regulatory networks, variant annotation, phylogenetic analysis
• 🧪 Cheminformatics & Drug Discovery - Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization
• 🔬 Proteomics & Mass Spectrometry - LC-MS/MS processing, peptide identification, spectral matching, protein quantification
• 🏥 Clinical Research & Precision Medicine - Clinical trials, pharmacogenomics, variant interpretation, drug safety, clinical decision support, treatment planning
• 🧠 Healthcare AI & Clinical ML - EHR analysis, physiological signal processing, medical imaging, clinical prediction models
• 🖼️ Medical Imaging & Digital Pathology - DICOM processing, whole slide image analysis, computational pathology, radiology workflows
• 🤖 Machine Learning & AI - Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods
• 🔮 Materials Science & Chemistry - Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry
• 🌌 Physics & Astronomy - Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations
• ⚙️ Engineering & Simulation - Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization
• 📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA
• 🌍 Geospatial Science & Remote Sensing - Satellite imagery processing, GIS analysis, spatial statistics, terrain analysis, machine learning for Earth observation
• 🧪 Laboratory Automation - Liquid handling protocols, lab equipment control, workflow automation, LIMS integration
• 📚 Scientific Communication - Literature review, peer review, scientific writing, document processing, posters, slides, schematics, citation management
• 🔬 Multi-omics & Systems Biology - Multi-modal data integration, pathway analysis, network biology, systems-level insights
• 🧬 Protein Engineering & Design - Protein language models, structure prediction, sequence design, function annotation
• 🎓 Research Methodology - Hypothesis generation, scientific brainstorming, critical thinking, grant writing, scholar evaluation

Transform your AI coding agent into an ‘AI Scientist’ on your desktop!

⭐ If you find this repository useful, please consider giving it a star! It helps others discover these tools and encourages us to continue maintaining and expanding this collection.

🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills (https://youtu.be/ZxbnDaD_FVg) video for a quick walkthrough.

📦 What’s Included

This repository provides 135 scientific and research skills organized into the following categories:

• 100+ Scientific & Financial Databases - A unified database-lookup skill provides direct access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, U.S. Treasury Fiscal Data, and Hugging Science (curated catalog of scientific datasets, models, and demos across 17 scientific domains on Hugging Face). Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage
• 70+ Optimized Python Package Skills - Explicitly defined skills for RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, pyzotero, BioServices, PennyLane, Qiskit, OpenMM, MDAnalysis, scVelo, TimesFM, and others — with curated documentation, examples, and best practices. Note: the agent can write code using any Python package, not just these; these skills simply provide stronger, more reliable performance for the packages listed
• 9 Scientific Integration Skills - Explicitly defined skills for Benchling, DNAnexus, LatchBio, OMERO, Protocols.io, Open Notebook, and more. Again, the agent is not limited to these — any API or platform reachable from Python is fair game; these skills are the optimized, pre-documented paths
• 30+ Analysis & Communication Tools - Literature review, scientific writing, peer review, document processing, posters, slides, schematics, infographics, Mermaid diagrams, and more
• 10+ Research & Clinical Tools - Hypothesis generation, grant writing, clinical decision support, treatment plans, regulatory compliance, scenario analysis

Each skill includes:

• ✅ Comprehensive documentation (SKILL.md)
• ✅ Practical code examples
• ✅ Use cases and best practices
• ✅ Integration guides
• ✅ Reference materials

📋 Table of Contents

• What’s Included
• Why Use This?
• Getting Started
• Security Disclaimer
• Support Open Source
• Prerequisites
• Quick Examples
• Use Cases
• Available Skills
• Contributing
• Troubleshooting
• FAQ
• Support
• Citation
• License

🚀 Why Use This?

⚡ Accelerate Your Research

• Save Days of Work - Skip API documentation research and integration setup
• Production-Ready Code - Tested, validated examples following scientific best practices
• Multi-Step Workflows - Execute complex pipelines with a single prompt

🎯 Comprehensive Coverage

• 135 Skills - Extensive coverage across all major scientific domains
• 100+ Databases - Unified access to 78+ databases via database-lookup, plus dedicated data access skills and multi-database packages like BioServices, BioPython, and gget
• 70+ Optimized Python Package Skills - RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioServices, PennyLane, Qiskit, OpenMM, scVelo, TimesFM, and others (the agent can use any Python package; these are the pre-documented, higher-performing paths)

🔧 Easy Integration

• Simple Setup - Copy skills to your skills directory and start working
• Automatic Discovery - Your agent automatically finds and uses relevant skills
• Well Documented - Each skill includes examples, use cases, and best practices

🌟 Maintained & Supported

• Regular Updates - Continuously maintained and expanded by K-Dense team
• Community Driven - Open source with active community contributions
• Enterprise Ready - Commercial support available for advanced needs

🎯 Getting Started

Option 1: npx (all platforms)

Install Scientific Agent Skills with a single command:

bash npx skills add K-Dense-AI/scientific-agent-skills

This is the official standard approach for installing Agent Skills across all platforms, including Claude Code, Claude Cowork, Codex, Gemini CLI, Cursor, and any other agent that supports the open Agent Skills (https://agentskills.io/) standard.

Option 2: GitHub CLI (gh skill)

If you use the GitHub CLI (https://cli.github.com/) (v2.90.0+), you can install skills with gh skill (https://github.blog/changelog/2026-04-16-manage-agent-skills-with-github-cli/):

``bash

Browse and install interactively

gh skill install K-Dense-AI/scientific-agent-skills

Install a specific skill directly

gh skill install K-Dense-AI/scientific-agent-skills scanpy

Target a specific agent host

gh skill install K-Dense-AI/scientific-agent-skills –agent cursor gh skill install K-Dense-AI/scientific-agent-skills –agent claude-code gh skill install K-Dense-AI/scientific-agent-skills –agent codex gh skill install K-Dense-AI/scientific-agent-skills –agent gemini ``

gh skill automatically installs to the correct directory for your agent host and records provenance metadata for supply chain integrity.

Version pinning

Pin to a specific release tag or commit SHA for reproducible installs:

``bash

Pin to a release tag

gh skill install K-Dense-AI/scientific-agent-skills –pin v1.0.0

Pin to a commit SHA

gh skill install K-Dense-AI/scientific-agent-skills –pin abc123def ``

Keeping skills up to date

``bash

Check for updates interactively

gh skill update

Update all installed skills

gh skill update –all ``

That’s it! Your AI agent will automatically discover the skills and use them when relevant to your scientific tasks. You can also invoke any skill manually by mentioning the skill name in your prompt.

⚠️ Security Disclaimer

Skills can execute code and influence your coding agent’s behavior. Review what you install.

Agent Skills are powerful — they can instruct your AI agent to run arbitrary code, install packages, make network requests, and modify files on your system. A malicious or poorly written skill has the potential to steer your coding agent into harmful behavior.

We take security seriously. All contributions go through a review process, and we run LLM-based security scans (via Cisco AI Defense Skill Scanner (https://github.com/cisco-ai-defense/skill-scanner)) on every skill in this repository. However, as a small team with a growing number of community contributions, we cannot guarantee that every skill has been exhaustively reviewed for all possible risks.

It is ultimately your responsibility to review the skills you install and decide which ones to trust.

We recommend the following:

• Do not install everything at once. Only install the skills you actually need for your work. While installing the full collection was reasonable when K-Dense created and maintained every skill, the repository now includes many community contributions that we may not have reviewed as thoroughly.
• Read the SKILL.md before installing. Each skill’s documentation describes what it does, what packages it uses, and what external services it connects to. If something looks suspicious, don’t install it.
• Check the contribution history. Skills authored by K-Dense (K-Dense-AI) have been through our internal review process. Community-contributed skills have been reviewed to the best of our ability, but with limited resources.
• Run the security scanner yourself. Before installing third-party skills, scan them locally: bash uv pip install cisco-ai-skill-scanner skill-scanner scan /path/to/skill --use-behavioral
• Report anything suspicious. If you find a skill that looks malicious or behaves unexpectedly, please open an issue (https://github.com/K-Dense-AI/scientific-agent-skills/issues) immediately so we can investigate.

All skills are scanned on an approximately weekly basis, and SECURITY.md is updated with the latest results. We try to address security gaps as they arise.

❤️ Support the Open Source Community

Scientific Agent Skills is powered by 50+ incredible open source projects maintained by dedicated developers and research communities worldwide. Projects like Biopython, Scanpy, RDKit, scikit-learn, PyTorch Lightning, and many others form the foundation of these skills.

If you find value in this repository, please consider supporting the projects that make it possible:

• ⭐ Star their repositories on GitHub
• 💰 Sponsor maintainers via GitHub Sponsors or NumFOCUS
• 📝 Cite projects in your publications
• 💻 Contribute code, docs, or bug reports

👉 View the full list of projects to support

⚙️ Prerequisites

• Python: 3.11+ (3.12+ recommended for best compatibility)
• uv: Python package manager (required for installing skill dependencies)
• Client: Any agent that supports the Agent Skills (https://agentskills.io/) standard (Cursor, Claude Code, Gemini CLI, Codex, etc.)
• System: macOS, Linux, or Windows with WSL2
• Dependencies: Automatically handled by individual skills (check SKILL.md files for specific requirements)

Installing uv

The skills use uv as the package manager for installing Python dependencies. Install it using the instructions for your operating system:

macOS and Linux: bash curl -LsSf https://astral.sh/uv/install.sh | sh

Windows: powershell powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Alternative (via pip): bash pip install uv

After installation, verify it works by running: bash uv --version

For more installation options and details, visit the official uv documentation (https://docs.astral.sh/uv/).

💡 Quick Examples

Once you’ve installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:

🧪 Drug Discovery Pipeline

Goal: Find novel EGFR inhibitors for lung cancer treatment

Prompt: `` Use available skills you have access to whenever possible. Query ChEMBL for EGFR inhibitors (IC50 < 50nM), analyze structure-activity relationships with RDKit, generate improved analogs with