claude-scientific-skills
The claude-scientific-skills collection provides 128+ ready-to-use scientific workflows across bioinformatics, cheminformatics, clinical research, medical imaging, machine learning, and materials science. Use this skill set when performing complex multi-step scientific analysis, literature research, molecular modeling, genomic data processing, or healthcare AI applications that require integration with specialized databases and Python scientific packages.
git clone --depth 1 https://github.com/Microck/ordinary-claude-skills /tmp/claude-scientific-skills && cp -r /tmp/claude-scientific-skills/skills_all/claude-scientific-skills ~/.claude/skills/claude-scientific-skillsSKILL.md
# Claude 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.
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.