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。
Performs end-to-end single-cell RNA-seq analysis workflows including quality control, clustering, and cell type annotation using the Scanpy toolkit.
Converts over 20 diverse file formats into LLM-optimized Markdown for seamless data processing and RAG integration.
Applies medicinal chemistry rules and structural filters to prioritize compound libraries for drug discovery.
Queries and retrieves comprehensive genomic data from NCBI Gene databases for biological research and bioinformatics.
Manages biological datasets with full lineage tracking, ontology-based validation, and FAIR compliance.
Identifies and analyzes profitable cryptocurrency arbitrage opportunities across centralized and decentralized exchanges in real-time.
Performs constraint-based metabolic modeling and analysis for systems biology and metabolic engineering using the COBRApy library.
Automates complex biomedical research tasks including genomics analysis, drug discovery, and clinical data interpretation through autonomous AI agents.
Streamlines machine learning workflows for genomic interval data using region embeddings, single-cell analysis, and consensus peak building.
Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets in Python.
Guides the architectural design and refactoring of AI agents into focused, single-purpose specialists to maximize performance and accuracy.
Conducts advanced machine learning research for Radio Access Networks using reinforcement learning, causal inference, and cognitive frameworks.
Streamlines computational molecular biology tasks and bioinformatics workflows using the Biopython library.
Enables advanced geospatial vector data analysis, geometric operations, and spatial mapping within Python environments.
Executes high-performance computational fluid dynamics simulations and analysis using pseudospectral methods in Python.
Automates the design, configuration, and implementation of complex neural network architectures for various machine learning tasks.
Implements high-performance persistent memory and pattern learning for stateful AI agents using AgentDB.
Optimizes 5G RAN mobility and handover performance using cognitive AI and predictive trajectory modeling.
Accesses and manages AI-ready drug discovery datasets, benchmarks, and molecular oracles for therapeutic machine learning.
Implements adaptive learning and meta-cognitive systems to enable AI agents to recognize patterns and optimize strategies through experience.
Simulates and analyzes quantum mechanical systems using the Quantum Toolbox in Python (QuTiP).
Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.
Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.
Analyzes biological data including sequences, phylogenetic trees, and microbial diversity metrics using specialized Python tools.
Provides comprehensive tools for astronomical data analysis, coordinate transformations, and cosmological calculations within Python environments.
Manipulates genomic datasets and processes Next-Generation Sequencing (NGS) files using a Pythonic interface to htslib.
Performs high-performance nonlinear dimensionality reduction for data visualization, clustering preprocessing, and supervised manifold learning.
Provides expert guidance and implementation patterns for crafting highly effective LLM prompts using advanced techniques like chain-of-thought and few-shot learning.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Analyzes Civitai video engagement and content metrics through natural language SQL queries and automated reporting.
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