A comprehensive collection of 134 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, 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, and more. Transform your AI agent int
Add this skill
npx mdskills install K-Dense-AI/scientific-agent-skillsComprehensive collection of 134 scientific research skills with excellent documentation and wide domain coverage
🔔 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 134 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.
A comprehensive collection of 134 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, 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, 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:
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 video for a quick walkthrough.
This repository provides 134 scientific and research skills organized into the following categories:
Each skill includes:
SKILL.md)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, Cursor, and any other agent that supports the open Agent Skills standard.
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.
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) 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:
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.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.uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/skill --use-behavioral
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:
👉 View the full list of projects to support
SKILL.md files for specific requirements)The skills use uv as the package manager for installing Python dependencies. Install it using the instructions for your operating system:
macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Alternative (via pip):
pip install uv
After installation, verify it works by running:
uv --version
For more installation options and details, visit the official uv documentation.
Once you've installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:
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 📖 **Want more examples?** Check out [docs/examples.md](docs/examples.md) for comprehensive workflow examples and detailed use cases across all scientific domains.
---
## 🚀 Want to Skip the Setup and Just Do the Science?
**Recognize any of these?**
- You spent more time configuring environments than running analyses
- Your workflow needs a GPU your local machine does not have
- You need a shareable, publication-ready figure or report, not just a script
- You want to run a complex multi-step pipeline right now, without reading package docs first
If so, **[K-Dense Web](https://k-dense.ai)** was built for you. It is the full AI co-scientist platform: everything in this repo plus cloud GPUs, 200+ skills, and outputs you can drop directly into a paper or presentation. Zero setup required.
| Feature | This Repo | K-Dense Web |
|---------|-----------|-------------|
| Scientific Skills | 134 skills | **200+ skills** (exclusive access) |
| Setup | Manual installation | **Zero setup, works instantly** |
| Compute | Your machine | **Cloud GPUs and HPC included** |
| Workflows | Prompt and code | **End-to-end research pipelines** |
| Outputs | Code and analysis | **Publication-ready figures, reports, and papers** |
| Integrations | Local tools | **Lab systems, ELNs, and cloud storage** |
> *"K-Dense Web took me from raw sequencing data to a draft figure in one afternoon. What used to take three days of environment setup and scripting now just works."*
> **Computational biologist, drug discovery**
> ### 💰 $50 in free credits, no credit card required
> Start running real scientific workflows in minutes.
>
> **[Try K-Dense Web free](https://k-dense.ai)**
*[k-dense.ai](https://k-dense.ai) | [Read the full comparison](https://k-dense.ai/blog/k-dense-web-vs-scientific-agent-skills)*
---
## 🔬 Use Cases
### 🧪 Drug Discovery & Medicinal Chemistry
- **Virtual Screening**: Screen millions of compounds from PubChem/ZINC against protein targets
- **Lead Optimization**: Analyze structure-activity relationships with RDKit, generate analogs with datamol
- **ADMET Prediction**: Predict absorption, distribution, metabolism, excretion, and toxicity with DeepChem
- **Molecular Docking**: Predict binding poses and affinities with DiffDock
- **Bioactivity Mining**: Query ChEMBL for known inhibitors and analyze SAR patterns
### 🧬 Bioinformatics & Genomics
- **Sequence Analysis**: Process DNA/RNA/protein sequences with BioPython and pysam
- **Single-Cell Analysis**: Analyze 10X Genomics data with Scanpy, identify cell types, infer GRNs with Arboreto
- **Variant Annotation**: Annotate VCF files with Ensembl VEP, query ClinVar for pathogenicity
- **Variant Database Management**: Build scalable VCF databases with TileDB-VCF for incremental sample addition, efficient population-scale queries, and compressed storage of genomic variant data
- **Gene Discovery**: Query NCBI Gene, UniProt, and Ensembl for comprehensive gene information
- **Network Analysis**: Identify protein-protein interactions via STRING, map to pathways (KEGG, Reactome)
### 🏥 Clinical Research & Precision Medicine
- **Clinical Trials**: Search ClinicalTrials.gov for relevant studies, analyze eligibility criteria
- **Variant Interpretation**: Annotate variants with ClinVar, COSMIC, and ClinPGx for pharmacogenomics
- **Drug Safety**: Query FDA databases for adverse events, drug interactions, and recalls
- **Precision Therapeutics**: Match patient variants to targeted therapies and clinical trials
### 🔬 Multi-Omics & Systems Biology
- **Multi-Omics Integration**: Combine RNA-seq, proteomics, and metabolomics data
- **Pathway Analysis**: Enrich differentially expressed genes in KEGG/Reactome pathways
- **Network Biology**: Reconstruct gene regulatory networks, identify hub genes
- **Biomarker Discovery**: Integrate multi-omics layers to predict patient outcomes
### 📊 Data Analysis & Visualization
- **Statistical Analysis**: Perform hypothesis testing, power analysis, and experimental design
- **Publication Figures**: Create publication-quality visualizations with matplotlib and seaborn
- **Network Visualization**: Visualize biological networks with NetworkX
- **Report Generation**: Generate comprehensive PDF reports with Document Skills
### 🧪 Laboratory Automation
- **Protocol Design**: Create Opentrons protocols for automated liquid handling
- **LIMS Integration**: Integrate with Benchling and LabArchives for data management
- **Workflow Automation**: Automate multi-step laboratory workflows
---
## 📚 Available Skills
This repository contains **134 scientific and research skills** organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.
### Skill Categories
> **Note:** The Python package and integration skills listed below are *explicitly defined* skills — curated with documentation, examples, and best practices for stronger, more reliable performance. They are not a ceiling: the agent can install and use *any* Python package or call *any* API, even without a dedicated skill. The skills listed simply make common workflows faster and more dependable.
#### 🧬 **Bioinformatics & Genomics** (21+ skills)
- Sequence analysis: BioPython, pysam, scikit-bio, BioServices
- Single-cell analysis: Scanpy, AnnData, scvi-tools, scVelo (RNA velocity), Arboreto, Cellxgene Census
- Genomic tools: gget, geniml, gtars, deepTools, FlowIO, Polars-Bio, Zarr, TileDB-VCF
- Differential expression: PyDESeq2
- Phylogenetics: ETE Toolkit, Phylogenetics (MAFFT, IQ-TREE 2, FastTree)
#### 🧪 **Cheminformatics & Drug Discovery** (10+ skills)
- Molecular manipulation: RDKit, Datamol, Molfeat
- Deep learning: DeepChem, TorchDrug
- Docking & screening: DiffDock
- Molecular dynamics: OpenMM + MDAnalysis (MD simulation & trajectory analysis)
- Cloud quantum chemistry: Rowan (pKa, docking, cofolding)
- Drug-likeness: MedChem
- Benchmarks: PyTDC
#### 🔬 **Proteomics & Mass Spectrometry** (2 skills)
- Spectral processing: matchms, pyOpenMS
#### 🏥 **Clinical Research & Precision Medicine** (8+ skills)
- Clinical databases: via Database Lookup (ClinicalTrials.gov, ClinVar, ClinPGx, COSMIC, FDA, cBioPortal, Monarch, and more)
- Cancer genomics: DepMap (cancer dependency scores, drug sensitivity)
- Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
- Healthcare AI: PyHealth, NeuroKit2, Clinical Decision Support
- Clinical documentation: Clinical Reports, Treatment Plans
#### 🖼️ **Medical Imaging & Digital Pathology** (3 skills)
- DICOM processing: pydicom
- Whole slide imaging: histolab, PathML
#### 🧠 **Neuroscience & Electrophysiology** (1 skill)
- Neural recordings: Neuropixels-Analysis (extracellular spikes, silicon probes, spike sorting)
#### 🤖 **Machine Learning & AI** (16+ skills)
- Deep learning: PyTorch Lightning, Transformers, Stable Baselines3, PufferLib
- Classical ML: scikit-learn, scikit-survival, SHAP
- Time series: aeon, TimesFM (Google's zero-shot foundation model for univariate forecasting)
- Bayesian methods: PyMC
- Optimization: PyMOO
- Graph ML: Torch Geometric
- Dimensionality reduction: UMAP-learn
- Statistical modeling: statsmodels
#### 🔮 **Materials Science, Chemistry & Physics** (7 skills)
- Materials: Pymatgen
- Metabolic modeling: COBRApy
- Astronomy: Astropy
- Quantum computing: Cirq, PennyLane, Qiskit, QuTiP
#### ⚙️ **Engineering & Simulation** (4 skills)
- Numerical computing: MATLAB/Octave
- Computational fluid dynamics: FluidSim
- Discrete-event simulation: SimPy
- Symbolic math: SymPy
#### 📊 **Data Analysis & Visualization** (16+ skills)
- Visualization: Matplotlib, Seaborn, Scientific Visualization
- Geospatial analysis: GeoPandas, GeoMaster (remote sensing, GIS, satellite imagery, spatial ML, 500+ examples)
- Data processing: Dask, Polars, Vaex
- Network analysis: NetworkX
- Document processing: Document Skills (PDF, DOCX, PPTX, XLSX)
- Infographics: Infographics (AI-powered professional infographic creation)
- Diagrams: Markdown & Mermaid Writing (text-based diagrams as default documentation standard)
- Exploratory data analysis: EDA workflows
- Statistical analysis: Statistical Analysis workflows
#### 🧪 **Laboratory Automation** (4 skills)
- Liquid handling: PyLabRobot
- Cloud lab: Ginkgo Cloud Lab (cell-free protein expression, fluorescent pixel art via autonomous RAC infrastructure)
- Protocol management: Protocols.io
- LIMS integration: Benchling, LabArchives
#### 🔬 **Multi-omics & Systems Biology** (4+ skills)
- Pathway analysis: via Database Lookup (KEGG, Reactome, STRING) and PrimeKG
- Multi-omics: HypoGeniC
- Data management: LaminDB
#### 🧬 **Protein Engineering & Design** (3 skills)
- Protein language models: ESM
- Glycoengineering: Glycoengineering (N/O-glycosylation prediction, therapeutic antibody optimization)
- Cloud laboratory platform: Adaptyv (automated protein testing and validation)
#### 📚 **Scientific Communication** (20+ skills)
- Literature: Paper Lookup (PubMed, PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall), Literature Review
- Advanced paper search: BGPT Paper Search (25+ structured fields per paper — methods, results, sample sizes, quality scores — from full text, not just abstracts)
- Web search: Perplexity Search (AI-powered search with real-time information), Parallel Web (synthesized summaries with citations)
- Research notebooks: Open Notebook (self-hosted NotebookLM alternative — PDFs, videos, audio, web pages; 16+ AI providers; multi-speaker podcast generation)
- Writing: Scientific Writing, Peer Review
- Document processing: XLSX, MarkItDown, Document Skills
- Publishing: Venue Templates
- Presentations: Scientific Slides, LaTeX Posters, PPTX Posters
- Diagrams: Scientific Schematics, Markdown & Mermaid Writing
- Infographics: Infographics (10 types, 8 styles, colorblind-safe palettes)
- Citations: Citation Management
- Illustration: Generate Image (AI image generation with FLUX.2 Pro and Gemini 3 Pro (Nano Banana Pro))
#### 🔬 **Scientific Databases & Data Access** (5 skills → 100+ databases total)
> A unified database-lookup skill provides direct REST API access to 78 public databases across all domains. Dedicated skills cover specialized data platforms. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage.
- Unified access: Database Lookup (78 databases spanning chemistry, genomics, clinical, pathways, patents, economics, and more — PubChem, ChEMBL, UniProt, PDB, AlphaFold, KEGG, Reactome, STRING, ClinVar, COSMIC, ClinicalTrials.gov, FDA, FRED, USPTO, SEC EDGAR, and dozens more)
- Cancer genomics: DepMap (cancer cell line dependencies, drug sensitivity, gene effect profiles)
- Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
- Knowledge graph: PrimeKG (precision medicine knowledge graph — genes, drugs, diseases, phenotypes)
- Fiscal data: U.S. Treasury Fiscal Data (national debt, Treasury statements, auctions, exchange rates)
#### 🔧 **Infrastructure & Platforms** (7+ skills)
- Cloud compute: Modal
- GPU acceleration: Optimize for GPU (CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, RAFT)
- Genomics platforms: DNAnexus, LatchBio
- Microscopy: OMERO
- Automation: Opentrons
- Resource detection: Get Available Resources
#### 🎓 **Research Methodology & Planning** (12+ skills)
- Ideation: Scientific Brainstorming, Hypothesis Generation
- Critical analysis: Scientific Critical Thinking, Scholar Evaluation
- Scenario analysis: What-If Oracle (multi-branch possibility exploration, risk analysis, strategic options)
- Multi-perspective deliberation: Consciousness Council (diverse expert viewpoints, devil's advocate analysis)
- Cognitive profiling: DHDNA Profiler (extract thinking patterns and cognitive signatures from any text)
- Funding: Research Grants
- Discovery: Research Lookup, Paper Lookup (10 academic databases)
- Market analysis: Market Research Reports
#### ⚖️ **Regulatory & Standards** (1 skill)
- Medical device standards: ISO 13485 Certification
> 📖 **For complete details on all skills**, see [docs/scientific-skills.md](docs/scientific-skills.md)
> 💡 **Looking for practical examples?** Check out [docs/examples.md](docs/examples.md) for comprehensive workflow examples across all scientific domains.
---
## 🤝 Contributing
We welcome contributions to expand and improve this scientific skills repository!
### Ways to Contribute
✨ **Add New Skills**
- Create skills for additional scientific packages or databases
- Add integrations for scientific platforms and tools
📚 **Improve Existing Skills**
- Enhance documentation with more examples and use cases
- Add new workflows and reference materials
- Improve code examples and scripts
- Fix bugs or update outdated information
🐛 **Report Issues**
- Submit bug reports with detailed reproduction steps
- Suggest improvements or new features
### How to Contribute
1. **Fork** the repository
2. **Create** a feature branch (`git checkout -b feature/amazing-skill`)
3. **Follow** the existing directory structure and documentation patterns
4. **Ensure** all new skills include comprehensive `SKILL.md` files
5. **Test** your examples and workflows thoroughly
6. **Commit** your changes (`git commit -m 'Add amazing skill'`)
7. **Push** to your branch (`git push origin feature/amazing-skill`)
8. **Submit** a pull request with a clear description of your changes
### Contribution Guidelines
✅ **Adhere to the [Agent Skills Specification](https://agentskills.io/specification)** — Every skill must follow the official spec (valid `SKILL.md` frontmatter, naming conventions, directory structure)
✅ Maintain consistency with existing skill documentation format
✅ Ensure all code examples are tested and functional
✅ Follow scientific best practices in examples and workflows
✅ Update relevant documentation when adding new capabilities
✅ Provide clear comments and docstrings in code
✅ Include references to official documentation
### Security Scanning
All skills in this repository are security-scanned using [Cisco AI Defense Skill Scanner](https://github.com/cisco-ai-defense/skill-scanner), an open-source tool that detects prompt injection, data exfiltration, and malicious code patterns in Agent Skills.
If you are contributing a new skill, we recommend running the scanner locally before submitting a pull request:
```bash
uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/your/skill --use-behavioral
Note: A clean scan result reduces noise in review, but does not guarantee a skill is free of all risk. Contributed skills are also reviewed manually before merging.
Contributors are recognized in our community and may be featured in:
Your contributions help make scientific computing more accessible and enable researchers to leverage AI tools more effectively!
This project builds on 50+ amazing open source projects. If you find value in these skills, please consider supporting the projects we depend on.
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SKILL.md fileProblem: Missing Python dependencies
SKILL.md file for required packagesuv pip install package-nameProblem: API rate limits
Problem: Authentication errors
SKILL.md for authentication setupProblem: Outdated examples
Q: Is this free to use?
A: Yes! This repository is MIT licensed. However, each individual skill has its own license specified in the license metadata field within its SKILL.md file—be sure to review and comply with those terms.
Q: Why are all skills grouped together instead of separate packages?
A: We believe good science in the age of AI is inherently interdisciplinary. Bundling all skills together makes it trivial for you (and your agent) to bridge across fields—e.g., combining genomics, cheminformatics, clinical data, and machine learning in one workflow—without worrying about which individual skills to install or wire together.
Q: Can I use this for commercial projects?
A: The repository itself is MIT licensed, which allows commercial use. However, individual skills may have different licenses—check the license field in each skill's SKILL.md file to ensure compliance with your intended use.
Q: Do all skills have the same license?
A: No. Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.
Q: How often is this updated?
A: We regularly update skills to reflect the latest versions of packages and APIs. Major updates are announced in release notes.
Q: Can I use this with other AI models?
A: The skills follow the open Agent Skills standard and work with any compatible agent, including Cursor, Claude Code, and Codex.
Q: Do I need all the Python packages installed?
A: No! Only install the packages you need. Each skill specifies its requirements in its SKILL.md file.
Q: What if a skill doesn't work?
A: First check the Troubleshooting section. If the issue persists, file an issue on GitHub with detailed reproduction steps.
Q: Do the skills work offline?
A: Database skills require internet access to query APIs. Package skills work offline once Python dependencies are installed.
Q: Can I contribute my own skills?
A: Absolutely! We welcome contributions. See the Contributing section for guidelines and best practices.
Q: How do I report bugs or suggest features?
A: Open an issue on GitHub with a clear description. For bugs, include reproduction steps and expected vs actual behavior.
Need help? Here's how to get support:
SKILL.md and references/ foldersWe'd love to have you join us! 🚀
Connect with other scientists, researchers, and AI enthusiasts using AI agents for scientific computing. Share your discoveries, ask questions, get help with your projects, and collaborate with the community!
Whether you're just getting started or you're a power user, our community is here to support you. We share tips, troubleshoot issues together, showcase cool projects, and discuss the latest developments in AI-powered scientific research.
See you there! 💬
If you use Scientific Agent Skills in your research or project, please cite it as:
@software{scientific_agent_skills_2026,
author = {{K-Dense Inc.}},
title = {Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents},
year = {2026},
url = {https://github.com/K-Dense-AI/scientific-agent-skills},
note = {134 skills covering databases, packages, integrations, and analysis tools}
}
K-Dense Inc. (2026). Scientific Agent Skills: A comprehensive collection of scientific tools for AI agents [Computer software]. https://github.com/K-Dense-AI/scientific-agent-skills
K-Dense Inc. Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents. 2026, github.com/K-Dense-AI/scientific-agent-skills.
Scientific Agent Skills by K-Dense Inc. (2026)
Available at: https://github.com/K-Dense-AI/scientific-agent-skills
We appreciate acknowledgment in publications, presentations, or projects that benefit from these skills!
This project is licensed under the MIT License.
Copyright © 2026 K-Dense Inc. (k-dense.ai)
See LICENSE.md for full terms.
⚠️ Important: Each skill has its own license specified in the
licensemetadata field within itsSKILL.mdfile. These licenses may differ from the repository's MIT License and may include additional terms or restrictions. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.
Install via CLI
npx mdskills install K-Dense-AI/scientific-agent-skillsScientific Agent Skills is a free, open-source AI agent skill. A comprehensive collection of 134 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, 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, and more. Transform your AI agent int
Install Scientific Agent Skills with a single command:
npx mdskills install K-Dense-AI/scientific-agent-skillsThis downloads the skill files into your project and your AI agent picks them up automatically.
Scientific Agent Skills works with Claude Code, Claude Desktop, Cursor, Vscode Copilot, Windsurf, Continue Dev, Codex, Gemini Cli, Amp, Roo Code, Goose, Opencode, Trae, Qodo, Command Code. Skills use the open SKILL.md format which is compatible with any AI coding agent that reads markdown instructions.