发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Empowers AI agents to conduct scientific research by providing standardized access to over 600 bioinformatics, cheminformatics, and genomics tools.
Manages annotated data matrices for single-cell genomics and large-scale biological datasets using the AnnData Python framework.
Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Queries and retrieves comprehensive gene information from NCBI databases for genomic research and functional analysis.
Builds and orchestrates end-to-end MLOps pipelines from data preparation through production deployment.
Accelerates drug discovery and molecular research using graph neural networks and PyTorch-based machine learning.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground LLM responses in proprietary data.
Accesses and analyzes functional genomics data from the NCBI Gene Expression Omnibus (GEO) repository.
Manage large-scale N-dimensional arrays with chunking, compression, and cloud-native storage for scientific computing workflows.
Implements professional machine learning workflows in Python using scikit-learn for classification, regression, clustering, and data preprocessing.
Architects sophisticated LLM applications using the LangChain framework for agents, memory management, and complex tool integration.
Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Implements lightweight dataset tracking and reproducibility patterns to ensure data changes are explicit and traceable.
Build, fit, and validate complex Bayesian probabilistic models using the PyMC Python library and modern MCMC sampling techniques.
Identifies and removes duplicate or visually similar images in FiftyOne datasets using deep learning embeddings.
Implements high-performance adaptive learning and experience replay for AI agents using the AgentDB vector engine.
Orchestrates dynamic AI context, intelligent memory systems, and RAG workflows for enterprise-scale multi-agent applications.
Accelerates reinforcement learning workflows with high-performance training, optimized environment vectorization, and seamless multi-agent support.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Manages complex Excel workbooks with automated formula recalculation, professional financial modeling standards, and deep data analysis capabilities.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the core Astropy Python library.
Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and structured output in production environments.
Monitors and summarizes the Nixtla forecasting ecosystem to provide actionable updates on TimeGPT, StatsForecast, and MLForecast.
Combines vector similarity and keyword-based search to improve retrieval accuracy in RAG systems and search engines.
Builds and trains sophisticated Graph Neural Networks (GNNs) using the PyTorch Geometric library for irregular data structures.
Implement high-performance similarity search and vector retrieval patterns across multiple database providers.
Implements sophisticated autonomous agent architectures and workflow patterns using the Vercel AI SDK.
Designs framework-agnostic, portable AI agents and multi-agent workflows using Oracle's Open Agent Specification.
Designs and implements sophisticated LLM applications using LangChain's framework for agents, memory, and complex workflows.
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