claude-scientific-skills
Scannednpx machina-cli add skill Microck/ordinary-claude-skills/claude-scientific-skills --openclawClaude Scientific Skills Collection
A comprehensive collection of 128+ ready-to-use scientific skills that transforms Claude into an AI research assistant capable of executing complex multi-step scientific workflows.
Scientific Domains
🧬 Bioinformatics & Genomics
Sequence analysis, single-cell RNA-seq, gene regulatory networks, variant annotation, phylogenetic analysis
🧪 Cheminformatics & Drug Discovery
Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization
🔬 Proteomics & Mass Spectrometry
LC-MS/MS processing, peptide identification, spectral matching, protein quantification
🏥 Clinical Research & Precision Medicine
Clinical trials, pharmacogenomics, variant interpretation, drug safety, precision therapeutics
🧠 Healthcare AI & Clinical ML
EHR analysis, physiological signal processing, medical imaging, clinical prediction models
🖼️ Medical Imaging & Digital Pathology
DICOM processing, whole slide image analysis, computational pathology, radiology workflows
🤖 Machine Learning & AI
Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods
🔮 Materials Science & Chemistry
Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry
🌌 Physics & Astronomy
Astronomical data analysis, cosmological calculations, symbolic mathematics, physics computations
⚙️ Engineering & Simulation
Discrete-event simulation, optimization, metabolic engineering, systems modeling
Included Skill Categories
- 26+ Scientific Databases - OpenAlex, PubMed, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov
- 54+ Python Packages - RDKit, Scanpy, PyTorch, scikit-learn, BioPython, PennyLane, Qiskit
- 15+ Scientific Integrations - Benchling, DNAnexus, LatchBio, OMERO, Protocols.io
- 20+ Analysis & Communication Tools - Literature review, scientific writing, peer review
Getting Started
Each skill within this collection includes:
- Comprehensive documentation (SKILL.md)
- Practical code examples
- Use cases and best practices
- Integration guides
- Reference materials
Explore the scientific-skills/ subdirectory for individual skill implementations and detailed documentation.
Source
git clone https://github.com/Microck/ordinary-claude-skills/blob/main/skills_all/claude-scientific-skills/SKILL.mdView on GitHub Overview
A comprehensive collection of 128+ ready-to-use scientific skills that turns Claude into an AI research assistant capable of executing complex, multi-step scientific workflows. It spans biology, chemistry, medicine, genomics, and advanced analysis domains, surfacing practical documentation, code samples, and integration guides to accelerate research.
How This Skill Works
Claude exposes modular skill implementations across key scientific domains such as bioinformatics, cheminformatics, proteomics, and clinical research. It relies on curated SKILL.md documentation, practical code examples, and integration with platforms like Benchling, DNAnexus, LatchBio, and OMERO to assemble end-to-end workflows that leverage common data sources and Python packages such as RDKit, Scanpy, PyTorch, and scikit-learn.
When to Use It
- When starting a multi-domain research project that spans biology, chemistry, and clinical data.
- When performing end-to-end genomics analyses including sequence analysis, single-cell RNA-seq, and variant annotation.
- When running drug discovery workflows with molecular property prediction, virtual screening, and ADMET analysis.
- When conducting proteomics workflows involving LC-MS/MS data processing and spectral matching.
- When building healthcare AI models or medical imaging analytics that leverage EHR data, DICOM, and whole slide image analysis.
Quick Start
- Step 1: Browse the claude-scientific-skills SKILL.md to identify relevant domain modules.
- Step 2: Connect Claude to your databases and tools using the provided integration guides.
- Step 3: Run a small end-to-end workflow with sample data, review outputs, and iterate.
Best Practices
- Start with modular skill chains and validate outputs on small, representative datasets.
- Consult the included SKILL.md documentation and use-case guidance before scaling workflows.
- Utilize recommended integrations with Benchling, DNAnexus, LatchBio, and OMERO to streamline pipelines.
- Cross-check results with reference materials from OpenAlex, PubMed, and ChEMBL to ensure credibility.
- Maintain versioned configurations and reproducible pipelines for audit and reuse.
Example Use Cases
- Genomics workflow combining sequence analysis, single-cell RNA-seq processing, and gene regulatory network inference.
- Drug discovery pipeline performing molecular docking and deep learning-based property predictions.
- Proteomics analysis workflow handling LC-MS/MS data, peptide identification, and spectral matching.
- Clinical research study incorporating pharmacogenomics variant interpretation and drug safety assessment.
- Healthcare AI project integrating EHR data with medical imaging analytics for predictive modeling.
