skills/claude-scientific-skills
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Added support for NeuroKit2

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Timothy Kassis
2025-10-25 21:38:31 -07:00
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README.md
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# Claude Scientific Skills
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.md)
[![Skills](https://img.shields.io/badge/Skills-87-brightgreen.svg)](#what-s-included)
[![Skills](https://img.shields.io/badge/Skills-90-brightgreen.svg)](#what-s-included)
[![Equivalent Tools](https://img.shields.io/badge/Equivalent_Tools-500-blue.svg)](#what-s-included)
A comprehensive collection of ready-to-use scientific skills for Claude, curated by the K-Dense team.
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| Category | Count | Description |
|----------|-------|-------------|
| 📊 **Scientific Databases** | 26 | PubMed, PubChem, UniProt, ChEMBL, COSMIC, DrugBank, AlphaFold DB, bioRxiv, and more |
| 🔬 **Scientific Packages** | 53 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, SimPy, pydicom, PyHealth, Data Commons, histolab, PathML, 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 |
@@ -78,7 +78,7 @@ Then, to install a specific set of skills:
2. Select **claude-scientific-skills**
3. Choose from:
- `scientific-databases` - Access to 25 scientific databases
- `scientific-packages` - 50 specialized Python packages
- `scientific-packages` - 51 specialized Python packages
- `scientific-thinking` - Analysis tools and document processing
- `scientific-integrations` - Lab automation and platform integrations
- `scientific-context-initialization` - Ensures Claude searches for and uses existing skills
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---
### 🔬 Scientific Packages
**50 specialized Python packages** organized by domain.
**56 specialized Python packages** organized by domain.
📖 **[Full Package Documentation →](docs/scientific-packages.md)**
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</details>
<details>
<summary><strong>Machine Learning & Deep Learning (10 packages)</strong></summary>
<summary><strong>Machine Learning & Deep Learning (11 packages)</strong></summary>
- PyMC, PyMOO, PyTorch Lightning, scikit-learn, scikit-survival, SHAP
- aeon, PyMC, PyMOO, PyTorch Lightning, scikit-learn, scikit-survival, SHAP
- statsmodels, Torch Geometric, Transformers, UMAP-learn
</details>
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</details>
<details>
<summary><strong>Additional Packages (8 packages)</strong></summary>
<summary><strong>Additional Packages (9 packages)</strong></summary>
- 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)