skills/claude-scientific-skills
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Add support for PufferLib which is a high-performance, open-source Python toolkit that wraps complex RL environments to look like standard Gym/PettingZoo interfaces and supports massively parallel simulation (1M+ steps/sec) to accelerate deep reinforcement learning.

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Timothy Kassis
2025-11-02 14:52:06 -08:00
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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-96-brightgreen.svg)](#what-s-included)
[![Skills](https://img.shields.io/badge/Skills-98-brightgreen.svg)](#what-s-included)
[![Equivalent Tools](https://img.shields.io/badge/Equivalent_Tools-1002-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** | 61 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, NetworkX, SimPy, pydicom, PyHealth, Data Commons, histolab, LaminDB, PathML, PyLabRobot, HypoGeniC, MarkItDown, Vaex, and more |
| 🔬 **Scientific Packages** | 63 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, NetworkX, SimPy, pydicom, PyHealth, Data Commons, histolab, LaminDB, PathML, PyLabRobot, HypoGeniC, MarkItDown, PufferLib, Stable Baselines3, Vaex, and more |
| 🔌 **Scientific Integrations** | 7 | Benchling, DNAnexus, Opentrons, LabArchives, LatchBio, OMERO, Protocols.io |
| 🛠️ **Scientific Helpers** | 2 | Context initialization and resource detection utilities |
| 📚 **Documented Workflows** | 122 | Ready-to-use examples and reference materials |
@@ -313,7 +313,7 @@ results, conclusions and providing recommendations."
---
### 🔬 Scientific Packages
**62 specialized Python packages** organized by domain.
**63 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 (11 packages)</strong></summary>
<summary><strong>Machine Learning & Deep Learning (13 packages)</strong></summary>
- aeon, PyMC, PyMOO, PyTorch Lightning, scikit-learn, scikit-survival, SHAP
- statsmodels, Torch Geometric, Transformers, UMAP-learn
- aeon, PufferLib, PyMC, PyMOO, PyTorch Lightning, scikit-learn, scikit-survival, SHAP
- Stable Baselines3, statsmodels, Torch Geometric, Transformers, UMAP-learn
</details>