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。
Automates the creation, editing, and professional formatting of complex Excel workbooks and financial models with full formula support.
Optimizes LangGraph application performance through iterative prompt engineering and node-level logic refinements based on quantitative evaluation criteria.
Analyzes LangGraph application workflows to identify performance bottlenecks and propose architecture-level optimizations for cost, latency, and accuracy.
Facilitates the end-to-end development of sophisticated AI agents and stateful workflows using the LangGraph framework.
Automates the analysis, processing, and visualization of Excel spreadsheets using Python-based data science libraries.
Automates the creation, editing, and analysis of professional-grade Excel spreadsheets with precise formula management and financial modeling standards.
Builds production-grade AI agents using the Pydantic AI framework with robust type safety and structured outputs.
Analyzes CSV files to provide immediate statistical summaries and automated data visualizations using Python and pandas.
Automates spreadsheet creation, complex financial modeling, and data analysis with professional formatting and formula integrity.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Performs high-performance nonlinear dimensionality reduction for data visualization, clustering preprocessing, and supervised manifold learning.
Manipulates genomic datasets and processes Next-Generation Sequencing (NGS) files using a Pythonic interface to htslib.
Provides comprehensive tools for astronomical data analysis, coordinate transformations, and cosmological calculations within Python environments.
Analyzes biological data including sequences, phylogenetic trees, and microbial diversity metrics using specialized Python tools.
Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.
Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.
Simulates and analyzes quantum mechanical systems using the Quantum Toolbox in Python (QuTiP).
Implements adaptive learning and meta-cognitive systems to enable AI agents to recognize patterns and optimize strategies through experience.
Accesses and manages AI-ready drug discovery datasets, benchmarks, and molecular oracles for therapeutic machine learning.
Implements high-performance persistent memory and pattern learning for stateful AI agents using AgentDB.
Executes high-performance computational fluid dynamics simulations and analysis using pseudospectral methods in Python.
Streamlines computational molecular biology tasks and bioinformatics workflows using the Biopython library.
Conducts advanced machine learning research for Radio Access Networks using reinforcement learning, causal inference, and cognitive frameworks.
Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets in Python.
Streamlines machine learning workflows for genomic interval data using region embeddings, single-cell analysis, and consensus peak building.
Automates complex biomedical research tasks including genomics analysis, drug discovery, and clinical data interpretation through autonomous AI agents.
Performs constraint-based metabolic modeling and analysis for systems biology and metabolic engineering using the COBRApy library.
Manages biological datasets with full lineage tracking, ontology-based validation, and FAIR compliance.
Queries and retrieves comprehensive genomic data from NCBI Gene databases for biological research and bioinformatics.
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