data science & ml向けのClaudeスキルを発見してください。53個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Automates complex biomedical research tasks including genomics, drug discovery, and clinical analysis through autonomous reasoning and code execution.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic wrapper for RDKit.
Processes and analyzes high-performance genomic interval data using Rust-powered algorithms and Python bindings.
Enables advanced protein engineering through generative design, structure prediction, and high-performance embeddings using ESM3 and ESM C models.
Simplifies PDF manipulation, data extraction, and document generation using industry-standard Python libraries and CLI tools.
Predicts 3D protein-ligand binding poses using state-of-the-art diffusion models for structure-based drug discovery.
Transforms, cleans, and reshapes complex datasets locally using industry-standard Python libraries like pandas and numpy.
Processes and prepares gigapixel whole slide images for digital pathology and machine learning workflows.
Develops and deploys specialized machine learning models for healthcare using clinical datasets and medical coding systems.
Builds and optimizes complex discrete-event simulations using the SimPy framework for Python.
Provides unified access to 20+ genomic databases for sequence analysis, protein structure prediction, and rapid bioinformatics queries.
Simplifies complex molecular informatics workflows by providing a Pythonic interface for RDKit with sensible defaults and built-in parallelization.
Simplifies molecular cheminformatics workflows by providing a Pythonic abstraction layer over RDKit for drug discovery and molecular analysis.
Integrates the open-source embedding database to build AI-native applications with semantic search and retrieval-augmented generation (RAG) capabilities.
Automates tissue detection and tile extraction from gigapixel histopathology images for computational pathology deep learning pipelines.
Provides unified access to 20+ genomic databases and analysis methods for rapid bioinformatics research and sequence analysis.
Streamlines computational molecular biology tasks including sequence analysis, NCBI database integration, and structural protein modeling.
Builds discrete-event simulation models in Python to analyze complex systems involving queues, resources, and time-based processes.
Simplifies molecular cheminformatics and drug discovery workflows with a Pythonic abstraction layer over RDKit.
Infers gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Manages local and self-hosted vector embeddings for RAG-based AI applications and semantic search.
Provides specialized tools for biological computation, sequence analysis, and programmatic access to NCBI databases using Biopython.
Builds, simulates, and executes quantum circuits using Google’s open-source framework for NISQ-era quantum computers.
Designs, simulates, and executes quantum circuits on simulators and real quantum hardware using Google's Cirq framework.
Performs advanced biological computation, sequence analysis, and programmatic access to NCBI databases using Python.
Develops, tests, and deploys machine learning models for clinical healthcare data using standardized pipelines and specialized medical architectures.
Simplifies astronomical data processing and astrophysical calculations using the core Python Astropy library.
Integrates Google's Gemini models into Claude Code for advanced reasoning and multi-model code analysis.
Develops, simulates, and optimizes quantum circuits for execution on diverse quantum hardware and high-performance simulators.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
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