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scientific-skills

Collection of scientific skills

Publisher marketplacescientific-skills@claude-scientific-skills · marketplace and plugin share one repository (therealtimex/claude-scientific-skills)

npx claudepluginhub therealtimex/claude-scientific-skills --plugin scientific-skills

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What's Inside

Skills118

adaptyv

/adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

aeon

/aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

anndata

/anndata

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

arboreto

/arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

/astropy

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

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Version1.0.0

LanguagePython

Stars0

MaintenanceUnmaintained

LicenseMIT

Last CommitDec 7, 2025

AddedJul 19, 2026

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claude-scientific-skills

README

Claude Scientific Skills

💼 For substantially more advanced capabilities, compute infrastructure, and enterprise-ready offerings, check out k-dense.ai.

A comprehensive collection of 125+ ready-to-use scientific skills for Claude, created by the K-Dense team. Transform Claude into your AI research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.

These skills enable Claude to seamlessly work with specialized scientific libraries, databases, and tools across multiple 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, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations
⚙️ Engineering & Simulation - Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization
📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing
🧪 Laboratory Automation - Liquid handling protocols, lab equipment control, workflow automation, LIMS integration
📚 Scientific Communication - Literature review, peer review, scientific writing, document processing, publication workflows
🔬 Multi-omics & Systems Biology - Multi-modal data integration, pathway analysis, network biology, systems-level insights
🧬 Protein Engineering & Design - Protein language models, structure prediction, sequence design, function annotation

Transform Claude Code into an 'AI Scientist' on your desktop!

⭐ If you find this repository useful, please consider giving it a star! It helps others discover these tools and encourages us to continue maintaining and expanding this collection.

📦 What's Included

This repository provides 125+ scientific skills organized into the following categories:

26+ Scientific Databases - Direct API access to OpenAlex, PubMed, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, and more
54+ Python Packages - RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, PennyLane, Qiskit, and others
15+ Scientific Integrations - Benchling, DNAnexus, LatchBio, OMERO, Protocols.io, and more
20+ Analysis & Communication Tools - Literature review, scientific writing, peer review, document processing

Each skill includes:

✅ Comprehensive documentation (SKILL.md)
✅ Practical code examples
✅ Use cases and best practices
✅ Integration guides
✅ Reference materials

🚀 Why Use This?

⚡ Accelerate Your Research

Save Days of Work - Skip API documentation research and integration setup
Production-Ready Code - Tested, validated examples following scientific best practices
Multi-Step Workflows - Execute complex pipelines with a single prompt

🎯 Comprehensive Coverage

125+ Skills - Extensive coverage across all major scientific domains
26+ Databases - Direct access to OpenAlex, PubMed, ChEMBL, UniProt, COSMIC, and more
54+ Python Packages - RDKit, Scanpy, PyTorch Lightning, scikit-learn, PennyLane, Qiskit, and others

View full README on GitHub

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scientific-skills

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