发现data science & ml类别的 Claude 技能。浏览 53 个技能,找到适合您 AI 工作流程的完美功能。
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.
Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.
Optimizes 5G RAN mobility and handover performance using cognitive AI and predictive trajectory modeling.
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.
Enables advanced geospatial vector data analysis, geometric operations, and spatial mapping within Python environments.
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.
Optimizes Radio Access Network performance using autonomous swarm coordination and cognitive temporal reasoning.
Streamlines machine learning workflows for genomic interval data using region embeddings, single-cell analysis, and consensus peak building.
Builds complex process-based discrete-event simulations in Python to model systems with shared resources and time-based events.
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.
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.
Performs end-to-end single-cell RNA-seq analysis workflows including quality control, clustering, and cell type annotation using the Scanpy toolkit.
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