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.
- What's Included
- Why Use This?
- Getting Started
- Prerequisites
- Quick Examples
- Use Cases
- Available Skills
- Contributing
- Troubleshooting
- FAQ
- Support
- Citation
- License
| Category | Count | Description |
|---|---|---|
| 📊 Scientific Databases | 26 | PubMed, PubChem, UniProt, ChEMBL, COSMIC, DrugBank, AlphaFold DB, bioRxiv, and more |
| 🔬 Scientific Packages | 56 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, SimPy, pydicom, PyHealth, Data Commons, histolab, PathML, PyLabRobot, and more |
| 🔌 Scientific Integrations | 6 | Benchling, DNAnexus, Opentrons, LabArchives, LatchBio, OMERO |
| 🛠️ Scientific Helpers | 2 | Context initialization and resource detection utilities |
| 📚 Documented Workflows | 122 | Ready-to-use examples and reference materials |
✅ 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
Register this repository as a Claude Code Plugin marketplace by running:
/plugin marketplace add K-Dense-AI/claude-scientific-skillsThen, to install a specific set of skills:
- Select Browse and install plugins
- Select claude-scientific-skills
- Choose from:
scientific-databases- Access to 25 scientific databasesscientific-packages- 51 specialized Python packagesscientific-thinking- Analysis tools and document processingscientific-integrations- Lab automation and platform integrationsscientific-context-initialization- Ensures Claude searches for and uses existing skills
- Select Install now
After installation, simply mention the skill or describe your task - Claude Code will automatically use the appropriate skills!
💡 Tip: If you find that Claude isn't utilizing the installed skills as much as you'd like, install the
scientific-context-initializationskill. It automatically creates/updates anAGENT.mdfile in your workspace that instructs Claude to always search for and use existing skills before attempting any scientific task. This ensures Claude leverages documented patterns, authentication methods, working examples, and best practices from the repository.
Use our newly released MCP server that allows you to use any Claude Skill in any client!
- 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)
Once you've installed the skills, you can ask Claude to execute complex multi-step scientific workflows:
"Always use available 'skills' when possible. Keep the output organized.
I need to find novel EGFR inhibitors for lung cancer treatment. Query ChEMBL for existing
EGFR inhibitors with IC50 < 50nM, analyze their structure-activity relationships using RDKit,
generate similar molecules with improved properties using datamol, perform virtual screening
with DiffDock against the AlphaFold-predicted EGFR structure, and search PubMed for recent
papers on resistance mechanisms to prioritize scaffolds. Finally, check COSMIC for common
EGFR mutations and assess how our candidates might interact with mutant forms.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
"Always use available 'skills' when possible. Keep the output organized.
Load this 10X Genomics dataset using Scanpy, perform quality control and doublet removal,
integrate with public data from Cellxgene Census for the same tissue type, identify cell
populations using known markers from NCBI Gene, perform differential expression analysis
with PyDESeq2, run gene regulatory network inference with Arboreto, query Reactome and
KEGG for pathway enrichment, and create publication-quality visualizations with matplotlib.
Then cross-reference top dysregulated genes with Open Targets to identify potential
therapeutic targets.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
"Always use available 'skills' when possible. Keep the output organized.
I have RNA-seq, proteomics, and metabolomics data from cancer patients. Use PyDESeq2 for
differential expression, pyOpenMS to analyze mass spec data, and integrate metabolite
information from HMDB and Metabolomics Workbench. Map proteins to pathways using UniProt
and KEGG, identify protein-protein interactions via STRING, correlate multi-omics layers
using statsmodels, and build a machine learning model with scikit-learn to predict patient
outcomes. Search ClinicalTrials.gov for ongoing trials targeting the top candidates.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
"Always use available 'skills' when possible. Keep the output organized.
I want to discover allosteric modulators for a protein-protein interaction. Retrieve the
AlphaFold structure for both proteins, identify the interaction interface using BioPython,
search ZINC15 for molecules with suitable properties for allosteric binding (MW 300-500,
logP 2-4), filter for drug-likeness using RDKit, perform molecular docking with DiffDock
to identify potential allosteric sites, rank candidates using DeepChem's property prediction
models, check PubChem for suppliers, and search USPTO patents to assess freedom to operate.
Finally, generate analogs with MedChem and molfeat for lead optimization.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
"Always use available 'skills' when possible. Keep the output organized.
Analyze this VCF file from a patient with suspected hereditary cancer. Use pysam to parse
variants, annotate with Ensembl for functional consequences, query ClinVar for known
pathogenic variants, check COSMIC for somatic mutations in cancer, retrieve gene information
from NCBI Gene, analyze protein impact using UniProt, search PubMed for case reports of
similar variants, query ClinPGx for pharmacogenomic implications, and generate a clinical
report with ReportLab. Then search ClinicalTrials.gov for precision medicine trials matching
the patient's profile.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
"Always use available 'skills' when possible. Keep the output organized.
Starting with a list of differentially expressed genes from my RNA-seq experiment, query
NCBI Gene for detailed annotations, retrieve protein sequences from UniProt, identify
protein-protein interactions using STRING, map to biological pathways in Reactome and KEGG,
analyze network topology with Torch Geometric, identify hub genes and bottleneck proteins,
perform gene regulatory network reconstruction with Arboreto, integrate with Open Targets
for druggability assessment, use PyMC for Bayesian network modeling, and create interactive
network visualizations. Finally, search GEO for similar expression patterns across diseases.
Create useful visualizations in the form of scientific figures as you go (if needed).
When done, create a comprehensive README.md and a well formatted pdf summarizing the methodology,
results, conclusions and providing recommendations."
- Screen compound libraries from PubChem and ZINC
- Analyze bioactivity data from ChEMBL
- Predict molecular properties with RDKit and DeepChem
- Perform molecular docking with DiffDock
- 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
- Analyze crystal structures with Pymatgen
- Predict material properties
- Design novel compounds and materials
- Search clinical trials on ClinicalTrials.gov
- Analyze genetic variants in ClinVar
- Review pharmacogenomic data from ClinPGx
- Access cancer mutations from COSMIC
- Literature searches via PubMed
- Patent landscape analysis using USPTO
- Data visualization for publications
- Statistical analysis and hypothesis testing
26 comprehensive databases including PubMed, PubChem, UniProt, ChEMBL, DrugBank, AlphaFold DB, bioRxiv, COSMIC, Ensembl, KEGG, and more.
📖 Full Database Documentation →
View all databases
- AlphaFold DB - AI-predicted protein structures (200M+ predictions)
- bioRxiv - Life sciences preprint server with medRxiv integration
- 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
- DrugBank - Comprehensive drug and drug target information
- 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
56 specialized Python packages organized by domain.
📖 Full Package Documentation →
Bioinformatics & Genomics (12 packages)
- AnnData, Arboreto, BioPython, BioServices, Cellxgene Census
- deepTools, FlowIO, gget, pysam, PyDESeq2, Scanpy, scvi-tools
Cheminformatics & Drug Discovery (8 packages)
- Datamol, DeepChem, DiffDock, MedChem, Molfeat, PyTDC, RDKit, TorchDrug
Proteomics & Mass Spectrometry (2 packages)
- matchms, pyOpenMS
Machine Learning & Deep Learning (11 packages)
- aeon, PyMC, PyMOO, PyTorch Lightning, scikit-learn, scikit-survival, SHAP
- statsmodels, Torch Geometric, Transformers, UMAP-learn
Materials Science & Chemistry (3 packages)
- Astropy, COBRApy, Pymatgen
Data Analysis & Visualization (6 packages)
- Dask, Matplotlib, Polars, ReportLab, Seaborn, SimPy
Additional Packages (9 packages)
- BIOMNI (Multi-omics), ETE Toolkit (Phylogenetics)
- histolab (Digital pathology WSI processing and tile extraction)
- Paper-2-Web (Academic paper dissemination and presentation)
- PathML (Computational pathology and whole-slide image analysis)
- PyLabRobot (Laboratory automation for liquid handlers, plate readers, and lab equipment)
- scikit-bio (Sequence analysis), ToolUniverse (600+ scientific tool ecosystem)
- Zarr (Array storage)
Comprehensive analysis tools and document processing capabilities.
📖 Full Thinking & Analysis Documentation →
Analysis & Methodology:
- Exploratory Data Analysis (automated statistics and insights)
- Hypothesis Generation (structured frameworks)
- Literature Review (systematic search and citation management)
- 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
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
2 helper utilities for enhanced scientific computing capabilities.
- scientific-context-initialization - Auto-invoked skill that creates/updates workspace AGENT.md to instruct Claude to search for and use existing skills before attempting any scientific task
- get-available-resources - Detects available system resources (CPU cores, GPUs, memory, disk space) and generates strategic recommendations for computational approaches (parallel processing, out-of-core computing, GPU acceleration)
We welcome contributions to expand and improve this scientific skills repository!
✨ 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
- 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
✅ 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
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)
Problem: Claude not using installed skills
- Solution: Install the
scientific-context-initializationskill - This creates an
AGENT.mdfile that instructs Claude to search for and use existing skills before attempting tasks - After installation, Claude will automatically leverage documented patterns, examples, and best practices
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
Q: Is this free to use?
A: Yes, for any purpose including commercial use. This project is MIT licensed.
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: Yes! The MIT License allows both commercial and noncommercial use without restrictions.
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.
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
If you use Claude Scientific Skills in your research or project, please cite it as:
@software{claude_scientific_skills_2025,
author = {{K-Dense Inc.}},
title = {Claude Scientific Skills: A Comprehensive Collection of Scientific Tools for Claude AI},
year = {2025},
url = {https://github.com/K-Dense-AI/claude-scientific-skills},
note = {skills covering databases, packages, integrations, and analysis tools}
}K-Dense Inc. (2025). Claude Scientific Skills: A comprehensive collection of scientific tools for Claude AI [Computer software]. https://github.com/K-Dense-AI/claude-scientific-skills
K-Dense Inc. Claude Scientific Skills: A Comprehensive Collection of Scientific Tools for Claude AI. 2025, github.com/K-Dense-AI/claude-scientific-skills.
Claude Scientific Skills by K-Dense Inc. (2025)
Available at: https://github.com/K-Dense-AI/claude-scientific-skills
We appreciate acknowledgment in publications, presentations, or projects that benefit from these skills!
This project is licensed under the MIT License.
Copyright © 2025 K-Dense Inc. (k-dense.ai)
- ✅ Free for any use (commercial and noncommercial)
- ✅ Open source - modify, distribute, and use freely
- ✅ Permissive - minimal restrictions on reuse
⚠️ No warranty - provided "as is" without warranty of any kind
See LICENSE.md for full terms.