15 KiB
Claude Scientific Skills
A comprehensive collection of ready-to-use scientific skills for Claude, curated by the K-Dense team.
These skills enable Claude to work with specialized scientific libraries and databases across multiple scientific domains:
- 🧬 Bioinformatics & Genomics
- 🧪 Cheminformatics & Drug Discovery
- 🔬 Proteomics & Mass Spectrometry
- 🤖 Machine Learning & AI
- 🔮 Materials Science & Chemistry
- 📊 Data Analysis & Visualization
Transform Claude Code into an 'AI Scientist' on your desktop!
💼 For substantially more advanced capabilities, compute infrastructure, and enterprise-ready offerings, check out k-dense.ai.
📋 Table of Contents
- What's Included
- Why Use This?
- Getting Started
- Prerequisites
- Quick Examples
- Use Cases
- Available Skills
- Contributing
- Troubleshooting
- FAQ
- Support
- License
📦 What's Included
| Category | Count | Description |
|---|---|---|
| 📊 Scientific Databases | 24 | PubMed, PubChem, UniProt, ChEMBL, COSMIC, AlphaFold DB, and more |
| 🔬 Scientific Packages | 41 | BioPython, RDKit, PyTorch, Scanpy, and specialized tools |
| 🔌 Scientific Integrations | 6 | Benchling, DNAnexus, Opentrons, LabArchives, LatchBio, OMERO |
| 📚 Documented Workflows | 122 | Ready-to-use examples and reference materials |
🚀 Why Use This?
✅ Save Time - Skip days of API documentation research and integration work
✅ Best Practices - Curated workflows following scientific computing standards
✅ Production Ready - Tested and validated code examples
✅ Regular Updates - Maintained and expanded by K-Dense team
✅ Comprehensive - Coverage across major scientific domains
✅ Enterprise Support - Commercial offerings available for advanced needs
🎯 Getting Started
Claude Code
Register this repository as a Claude Code Plugin marketplace by running:
/plugin marketplace add K-Dense-AI/claude-scientific-skills
Then, to install a specific set of skills:
- Select Browse and install plugins
- Select claude-scientific-skills
- Choose from:
scientific-databases- Access to 24 scientific databasesscientific-packages- 40 specialized Python packagesscientific-thinking- Analysis tools and document processingscientific-integrations- Lab automation and platform integrations
- Select Install now
After installation, simply mention the skill or describe your task - Claude Code will automatically use the appropriate skills!
Any MCP Client (including ChatGPT, Cursor, Google ADK, OpenAI Agent SDK, etc.)
Use our newly released MCP server that allows you to use any Claude Skill in any client!
⚙️ Prerequisites
- Python: 3.8+ (3.10+ recommended for best compatibility)
- Claude Code: Latest version or any MCP-compatible client
- System: macOS, Linux, or Windows with WSL2
- Dependencies: Automatically handled by individual skills (check
SKILL.mdfiles for specific requirements)
💡 Quick Examples
Once you've installed the skills, you can ask Claude:
Cheminformatics
"Use PubChem to find information about aspirin and calculate its molecular properties"
Bioinformatics
"Analyze this protein sequence using BioPython and predict its secondary structure"
Data Analysis
"Perform exploratory data analysis on this RNA-seq dataset and create publication-quality plots"
Drug Discovery
"Search ChEMBL for kinase inhibitors with IC50 < 100nM and visualize their structures"
Literature Review
"Search PubMed for recent papers on CRISPR-Cas9 applications in cancer therapy"
Protein Structure
"Retrieve the AlphaFold structure prediction for human p53 and analyze confidence scores"
🔬 Use Cases
Drug Discovery Research
- Screen compound libraries from PubChem and ZINC
- Analyze bioactivity data from ChEMBL
- Predict molecular properties with RDKit and DeepChem
- Perform molecular docking with DiffDock
Bioinformatics Analysis
- Process genomic sequences with BioPython
- Analyze single-cell RNA-seq data with Scanpy
- Query gene information from Ensembl and NCBI Gene
- Identify protein-protein interactions via STRING
Materials Science
- Analyze crystal structures with Pymatgen
- Predict material properties
- Design novel compounds and materials
Clinical Research
- Search clinical trials on ClinicalTrials.gov
- Analyze genetic variants in ClinVar
- Review pharmacogenomic data from ClinPGx
- Access cancer mutations from COSMIC
Academic Research
- Literature searches via PubMed
- Patent landscape analysis using USPTO
- Data visualization for publications
- Statistical analysis and hypothesis testing
📚 Available Skills
🗄️ Scientific Databases
24 comprehensive databases including PubMed, PubChem, UniProt, ChEMBL, AlphaFold DB, COSMIC, Ensembl, KEGG, and more.
📖 Full Database Documentation →
View all databases
- AlphaFold DB - AI-predicted protein structures (200M+ predictions)
- ChEMBL - Bioactive molecules and drug-like properties
- ClinPGx - Clinical pharmacogenomics and gene-drug interactions
- ClinVar - Genomic variants and clinical significance
- ClinicalTrials.gov - Global clinical studies registry
- COSMIC - Somatic cancer mutations database
- ENA - European Nucleotide Archive
- Ensembl - Genome browser and annotations
- FDA Databases - Drug approvals, adverse events, recalls
- GEO - Gene expression and functional genomics
- GWAS Catalog - Genome-wide association studies
- HMDB - Human metabolome database
- KEGG - Biological pathways and molecular interactions
- Metabolomics Workbench - NIH metabolomics data
- NCBI Gene - Gene information and annotations
- Open Targets - Therapeutic target identification
- PDB - Protein structure database
- PubChem - Chemical compound data (110M+ compounds)
- PubMed - Biomedical literature database
- Reactome - Curated biological pathways
- STRING - Protein-protein interaction networks
- UniProt - Protein sequences and annotations
- USPTO - Patent and trademark data
- ZINC - Commercially-available compounds for screening
🔬 Scientific Packages
41 specialized Python packages organized by domain.
📖 Full Package Documentation →
Bioinformatics & Genomics (11 packages)
- AnnData, Arboreto, BioPython, BioServices, Cellxgene Census
- deepTools, FlowIO, gget, pysam, PyDESeq2, Scanpy
Cheminformatics & Drug Discovery (8 packages)
- Datamol, DeepChem, DiffDock, MedChem, Molfeat, PyTDC, RDKit, TorchDrug
Proteomics & Mass Spectrometry (2 packages)
- matchms, pyOpenMS
Machine Learning & Deep Learning (8 packages)
- PyMC, PyMOO, PyTorch Lightning, scikit-learn, statsmodels
- Torch Geometric, Transformers, UMAP-learn
Materials Science & Chemistry (3 packages)
- Astropy, COBRApy, Pymatgen
Data Analysis & Visualization (5 packages)
- Dask, Matplotlib, Polars, ReportLab, Seaborn
Additional Packages (4 packages)
- BIOMNI (Multi-omics), ETE Toolkit (Phylogenetics)
- scikit-bio (Sequence analysis), Zarr (Array storage)
🧠 Scientific Thinking & Analysis
Comprehensive analysis tools and document processing capabilities.
📖 Full Thinking & Analysis Documentation →
Analysis & Methodology:
- Exploratory Data Analysis (automated statistics and insights)
- Hypothesis Generation (structured frameworks)
- Peer Review (comprehensive evaluation toolkit)
- Scientific Brainstorming (ideation workflows)
- Scientific Critical Thinking (rigorous reasoning)
- Scientific Visualization (publication-quality figures)
- Scientific Writing (IMRAD format, citation styles)
- Statistical Analysis (testing and experimental design)
Document Processing:
- DOCX, PDF, PPTX, XLSX manipulation and analysis
- Tracked changes, comments, and formatting preservation
- Text extraction, table parsing, and data analysis
🔌 Scientific Integrations
6 platform integrations for lab automation and workflow management.
📖 Full Integration Documentation →
- Benchling - R&D platform and LIMS integration
- DNAnexus - Cloud genomics and biomedical data analysis
- LabArchives - Electronic Lab Notebook (ELN) integration
- LatchBio - Workflow platform and cloud execution
- OMERO - Microscopy and bio-image data management
- Opentrons - Laboratory automation protocols
🤝 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
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-skill) - Follow the existing directory structure and documentation patterns
- Ensure all new skills include comprehensive
SKILL.mdfiles - Test your examples and workflows thoroughly
- Commit your changes (
git commit -m 'Add amazing skill') - Push to your branch (
git push origin feature/amazing-skill) - Submit a pull request with a clear description of your changes
Contribution Guidelines
✅ Maintain consistency with existing skill documentation format
✅ Include practical, working examples in all contributions
✅ 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
Recognition
Contributors are recognized in our community and may be featured in:
- Repository contributors list
- Special mentions in release notes
- K-Dense community highlights
Your contributions help make scientific computing more accessible and enable researchers to leverage AI tools more effectively!
📖 Contributing Guidelines → (coming soon)
🔧 Troubleshooting
Common Issues
Problem: Skills not loading in Claude Code
- Solution: Ensure you've installed the latest version of Claude Code
- Try reinstalling the plugin:
/plugin marketplace add K-Dense-AI/claude-scientific-skills
Problem: Missing Python dependencies
- Solution: Check the specific
SKILL.mdfile for required packages - Install dependencies:
pip install package-name
Problem: API rate limits
- Solution: Many databases have rate limits. Review the specific database documentation
- Consider implementing caching or batch requests
Problem: Authentication errors
- Solution: Some services require API keys. Check the
SKILL.mdfor authentication setup - Verify your credentials and permissions
Problem: Outdated examples
- Solution: Report the issue via GitHub Issues
- Check the official package documentation for updated syntax
❓ FAQ
Q: Is this free to use?
A: Yes, for noncommercial use. See the License section for details.
Q: Do I need all the Python packages installed?
A: No, only install the packages you need. Each skill specifies its requirements.
Q: Can I use this with other AI models?
A: The skills are designed for Claude but can be adapted for other models with MCP support.
Q: How often is this updated?
A: We regularly update skills to reflect the latest versions of packages and APIs.
Q: Can I use this for commercial projects?
A: For commercial use, please visit K-Dense for enterprise licensing.
Q: What if a skill doesn't work?
A: First check the troubleshooting section, then file an issue on GitHub with details.
Q: Can I contribute my own skills?
A: Absolutely! See the Contributing section for guidelines.
Q: Do the skills work offline?
A: Database skills require internet access. Package skills work offline once dependencies are installed.
💬 Support
Need help? Here's how to get support:
- 📖 Documentation: Check the relevant
SKILL.mdandreferences/folders - 🐛 Bug Reports: Open an issue
- 💡 Feature Requests: Submit a feature request
- 💼 Enterprise Support: Contact K-Dense for commercial support
- 🌐 MCP Support: Visit the claude-skills-mcp repository
📄 License
This project is licensed under the PolyForm Noncommercial License 1.0.0.
Copyright © K-Dense Inc. (k-dense.ai)
Key Points:
- ✅ Free for noncommercial use (research, education, personal projects)
- ✅ Free for noncommercial organizations (universities, research institutions)
- ❌ Commercial use requires separate license (contact K-Dense)
See LICENSE.md for full terms.