data science & ml向けのClaudeスキルを発見してください。61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。
Optimizes Apache Spark data processing jobs through advanced partitioning, memory management, and shuffle tuning.
Transforms Claude into a specialized prompt architect for designing, optimizing, and debugging complex AI instructions and agent behaviors.
Builds complex process-based discrete-event simulations in Python to model systems with shared resources and time-based events.
Configures and optimizes vector databases for Retrieval-Augmented Generation (RAG) applications using the LangChain4J framework.
Accesses and analyzes chemical data from the world's largest open chemical database using PUG-REST and PubChemPy.
Access and interpret the Human Metabolome Database for metabolite identification, biomarker discovery, and clinical chemistry research.
Accesses the ZINC database of 230M+ purchasable compounds for drug discovery, molecular docking, and chemical informatics.
Empowers AI agents to perform complex scientific research tasks using a unified ecosystem of 600+ specialized tools and databases.
Queries the ESS-DeepDive fusion database to retrieve field-level metadata and dataset file information for environmental science research.
Automates professional spreadsheet creation, complex financial modeling, and advanced data analysis within Claude Code.
Builds sophisticated, interactive, and data-driven visualizations using the D3.js library for custom charts and complex diagrams.
Automates the creation, editing, and analysis of professional spreadsheets with support for complex formulas and financial modeling standards.
Recalibrates upcoming training sessions dynamically based on recent performance, user feedback, and safety constraints.
Performs exact symbolic computation, calculus, and equation solving in Python to handle complex mathematical formulas without numerical approximation.
Builds, optimizes, and executes quantum circuits and algorithms across various hardware providers and simulators.
Facilitates direct REST API access to the KEGG database for bioinformatics research, pathway analysis, and gene mapping.
Queries the NCBI Gene database to retrieve comprehensive genomic information, including sequences, annotations, and functional data.
Orchestrates sophisticated multi-agent systems with intelligent routing, handoffs, and collaborative workflows across AI providers.
Queries the NHGRI-EBI GWAS Catalog to retrieve genetic variant-trait associations and summary statistics.
Enables parallel and distributed computing for Python data science workflows to process datasets larger than available memory.
Generates professional, publication-quality statistical graphics and complex multi-panel data visualizations using Python's Seaborn library.
Facilitates advanced probabilistic modeling and analysis of single-cell omics data using deep generative models.
Optimizes LangGraph application performance through iterative prompt engineering and node-level logic refinements based on quantitative evaluation criteria.
Analyzes mass spectrometry data for proteomics and metabolomics workflows using the PyOpenMS library.
Streamlines machine learning workflows in Python by providing expert guidance on scikit-learn algorithms, data preprocessing, and production-ready pipelines.
Integrates with the NCBI Gene Expression Omnibus (GEO) to search, download, and analyze high-throughput functional genomics datasets.
Analyzes LangGraph application workflows to identify performance bottlenecks and propose architecture-level optimizations for cost, latency, and accuracy.
Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.
Facilitates the end-to-end development of sophisticated AI agents and stateful workflows using the LangGraph framework.
Accesses and retrieves nucleotide sequences, raw reads, and genome assemblies from the European Nucleotide Archive for bioinformatics workflows.
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