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