Scientific Agent Skills

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🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.

New: 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 147 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via Modal for heavy workloads. Get started here.

Stay up to date: Follow K-Dense on X, LinkedIn, and YouTube for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.

A comprehensive collection of 147 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 standard, created by K-Dense. Works with Cursor, Claude Code, Codex, Google Antigravity, and more. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.

⭐ Help make AI for science easier to discover: If Scientific Agent Skills saves you time, teaches your agent a workflow, or helps your lab move faster, please star this repository. A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community.

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 - 🧰 Agent Platforms & Infrastructure - Build on Pi with SDK, RPC, extensions, custom providers/models, packages, TUI components, and session tooling - 🎓 Research Methodology - Hypothesis generation, scientific brainstorming, critical thinking, grant writing, scholar evaluation

Transform your AI coding agent into an 'AI Scientist' on your desktop!

🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills video for a quick walkthrough.

📦 What's Included

This repository provides 147 scientific and research skills organized into the following categories:

• 100+ Scientific & Financial Databases - A unified database-lookup skill provides deterministic, provenance-rich 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, Molecular Dynamics (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, Ginkgo Cloud Lab, LabArchives, and Opentrons. 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, Paperzilla, PACSOMATIC, Exa Search, posters, slides, schematics, infographics, Mermaid diagrams, and more
• 10+ Research & Clinical Tools - Hypothesis generation, grant writing, clinical decision support, treatment plans, BIDS, regulatory compliance, scenario analysis, and workflow-derived skill drafting with Autoskill

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

• 147 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, Molecular Dynamics (OpenMM/MDAnalysis), 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:

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, Google Antigravity, Cursor, OpenClaw, NVIDIA NemoClaw, Hermes, Pi, and any other agent that supports the open Agent Skills standard.

Option 2: GitHub CLI (gh skill)

If you use the GitHub CLI (v2.90.0+), you can install skills with gh skill:

# 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:

# 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

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

Other Agent Skills hosts (OpenClaw, NemoClaw, Pi, Hermes, …)

You usually don't need anything host-specific. npx skills add (Option 1) installs into the shared ~/.agents/skills/ convention, and any compliant client that scans that directory — including OpenClaw, NVIDIA NemoClaw (an OpenClaw-based secure runtime), and Pi — discovers the skills automatically. Project-scoped installs land in .agents/skills/ and work the same way. To install without the CLI, clone straight into either location:

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git ~/.agents/skills/scientific-agent-skills   # user-level
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git .agents/skills/scientific-agent-skills      # project-level

Hermes is the one host that uses its own registry instead of the shared directory, so add the repo as a tap:

hermes skills tap add K-Dense-AI/scientific-agent-skills

These skills stay portable across all of them: metadata is single-line JSON (so OpenClaw's line-based reader parses it), credentialed skills declare a top-level required_environment_variables field (so Hermes prompts for