# Claude Scientific Skills
[](LICENSE.md)
[](https://github.com/K-Dense-AI/claude-scientific-skills)
[](#what-s-included)
[](#what-s-included)
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:
- ๐งฌ Bioinformatics & Genomics
- ๐งช Cheminformatics & Drug Discovery
- ๐ฌ Proteomics & Mass Spectrometry
- ๐ค Machine Learning & AI
- ๐ฎ Materials Science & Chemistry
- ๐ Data Analysis & Visualization
**Transform Claude Code into an 'AI Scientist' on your desktop!**
> ๐ผ For substantially more advanced capabilities, compute infrastructure, and enterprise-ready offerings, check out [k-dense.ai](https://k-dense.ai/).
---
## ๐ Table of Contents
- [What's Included](#what-s-included)
- [Why Use This?](#why-use-this)
- [Getting Started](#getting-started)
- [Claude Code](#claude-code)
- [Any MCP Client](#any-mcp-client-including-chatgpt-cursor-google-adk-openai-agent-sdk-etc)
- [Prerequisites](#prerequisites)
- [Quick Examples](#quick-examples)
- [Use Cases](#use-cases)
- [Available Skills](#available-skills)
- [Contributing](#contributing)
- [Troubleshooting](#troubleshooting)
- [FAQ](#faq)
- [Support](#support)
- [License](#license)
---
## ๐ฆ What's Included
| Category | Count | Description |
|----------|-------|-------------|
| ๐ **Scientific Databases** | 24 | PubMed, PubChem, UniProt, ChEMBL, COSMIC, AlphaFold DB, and more |
| ๐ฌ **Scientific Packages** | 41 | BioPython, RDKit, PyTorch, Scanpy, and specialized tools |
| ๐ **Scientific Integrations** | 6 | Benchling, DNAnexus, Opentrons, LabArchives, LatchBio, OMERO |
| ๐ **Documented Workflows** | 122 | Ready-to-use examples and reference materials |
---
## ๐ Why Use This?
โ
**Save Time** - Skip days of API documentation research and integration work
โ
**Best Practices** - Curated workflows following scientific computing standards
โ
**Production Ready** - Tested and validated code examples
โ
**Regular Updates** - Maintained and expanded by K-Dense team
โ
**Comprehensive** - Coverage across major scientific domains
โ
**Enterprise Support** - Commercial offerings available for advanced needs
---
## ๐ฏ Getting Started
### Claude Code
Register this repository as a Claude Code Plugin marketplace by running:
```bash
/plugin marketplace add K-Dense-AI/claude-scientific-skills
```
Then, to install a specific set of skills:
1. Select **Browse and install plugins**
2. Select **claude-scientific-skills**
3. Choose from:
- `scientific-databases` - Access to 24 scientific databases
- `scientific-packages` - 40 specialized Python packages
- `scientific-thinking` - Analysis tools and document processing
- `scientific-integrations` - Lab automation and platform integrations
4. 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!
๐ **[claude-skills-mcp](https://github.com/K-Dense-AI/claude-skills-mcp)**
---
## โ๏ธ 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.md` files for specific requirements)
---
## ๐ก 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"
```
---
## ๐ฌ 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
---
## ๐ Available Skills
### ๐๏ธ Scientific Databases
**24 comprehensive databases** including PubMed, PubChem, UniProt, ChEMBL, AlphaFold DB, COSMIC, Ensembl, KEGG, and more.
๐ **[Full Database Documentation โ](docs/scientific-databases.md)**
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
---
### ๐ฌ Scientific Packages
**41 specialized Python packages** organized by domain.
๐ **[Full Package Documentation โ](docs/scientific-packages.md)**
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)
---
### ๐ง Scientific Thinking & Analysis
**Comprehensive analysis tools** and document processing capabilities.
๐ **[Full Thinking & Analysis Documentation โ](docs/scientific-thinking.md)**
**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
---
### ๐ Scientific Integrations
**6 platform integrations** for lab automation and workflow management.
๐ **[Full Integration Documentation โ](docs/scientific-integrations.md)**
- **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
---
## ๐ค Contributing
We welcome contributions to expand and improve this scientific skills repository!
### Ways to Contribute
โจ **Add New Skills**
- Create skills for additional scientific packages or databases
- Add integrations for scientific platforms and tools
๐ **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
๐ **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
โ
Maintain consistency with existing skill documentation format
โ
Include practical, working examples in all contributions
โ
Ensure all code examples are tested and functional
โ
Follow scientific best practices in examples and workflows
โ
Update relevant documentation when adding new capabilities
โ
Provide clear comments and docstrings in code
โ
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!
๐ **[Contributing Guidelines โ](CONTRIBUTING.md)** *(coming soon)*
---
## ๐ง 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.md` file 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.md` for 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
---
## โ FAQ
**Q: Is this free to use?**
A: Yes, for noncommercial use. See the [License](#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](https://k-dense.ai/) 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](#contributing) section for guidelines.
**Q: Do the skills work offline?**
A: Database skills require internet access. Package skills work offline once dependencies are installed.
---
## ๐ฌ Support
Need help? Here's how to get support:
- ๐ **Documentation**: Check the relevant `SKILL.md` and `references/` folders
- ๐ **Bug Reports**: [Open an issue](https://github.com/K-Dense-AI/claude-scientific-skills/issues)
- ๐ก **Feature Requests**: [Submit a feature request](https://github.com/K-Dense-AI/claude-scientific-skills/issues/new)
- ๐ผ **Enterprise Support**: Contact [K-Dense](https://k-dense.ai/) for commercial support
- ๐ **MCP Support**: Visit the [claude-skills-mcp](https://github.com/K-Dense-AI/claude-skills-mcp) repository
---
## ๐ License
This project is licensed under the **PolyForm Noncommercial License 1.0.0**.
**Copyright ยฉ K-Dense Inc.** ([k-dense.ai](https://k-dense.ai/))
### Key Points:
- โ
**Free for noncommercial use** (research, education, personal projects)
- โ
**Free for noncommercial organizations** (universities, research institutions)
- โ **Commercial use requires separate license** (contact K-Dense)
See [LICENSE.md](LICENSE.md) for full terms.