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。
Automates professional spreadsheet creation, editing, and data analysis with a focus on formula integrity and financial modeling standards.
Enables comprehensive spreadsheet creation, editing, and professional financial modeling using Python and LibreOffice.
Automates the creation, editing, and analysis of professional spreadsheets with advanced formula support and industry-standard financial modeling.
Automates professional spreadsheet creation, financial modeling, and data analysis with strict formula integrity and industry-standard formatting.
Masters AI image generation with expert guidance on prompt engineering, model configuration, and parameter optimization.
Integrates Alibaba Cloud DashScope AI services into Spring applications using the Spring AI framework.
Builds real-time data transformation pipelines and vector search indexes for AI applications.
Automates professional spreadsheet creation, data analysis, and financial modeling with rigorous formula verification and industry-standard formatting.
Crafts high-quality, structured AI prompts through collaborative architectural design and advanced engineering techniques like Chain-of-Thought and Step-Back prompting.
Automates the retrieval and processing of AORC historical data and Atlas 14 design storms for HEC-RAS and HEC-HMS hydrological models.
Parses and modifies HEC-RAS plain text geometry files using a Python API designed for hydraulic modeling automation.
Configures and optimizes LangChain4j vector stores for RAG applications, enabling seamless semantic search and embedding storage in Java environments.
Builds ultra-performant real-time ETL pipelines for AI indexing, vector search, and incremental data processing.
Provides specialized guidance for designing machine learning systems, computer vision pipelines, and production-ready AI architectures.
Automates the creation, editing, and analysis of professional Excel spreadsheets with advanced formula support and financial modeling standards.
Analyzes generated AI prompts to provide structural insights, style comparisons, and data-driven optimization recommendations.
Creates and optimizes advanced prompts using patterns like few-shot learning, chain-of-thought, and system prompt design to significantly improve LLM performance.
Ensures the integrity and quality of AI evaluation datasets through automated schema validation, duplicate detection, and coverage analysis.
Implements dynamic, state-based routing and retry logic for complex LangGraph AI agent workflows.
Implements robust state management patterns for LangGraph workflows using TypedDict, Pydantic, and custom reducers.
Coordinates complex multi-agent workflows using a central supervisor to route tasks among specialized LangGraph workers.
Implements robust human-in-the-loop patterns for LangGraph workflows to enable manual review gates and approval-based agent supervision.
Enables high-performance local LLM execution for cost-effective, private, and offline AI-powered development.
Automates the curation and quality validation of high-fidelity evaluation datasets using multi-agent analysis pipelines.
Enhances RAG retrieval accuracy by generating hypothetical answer documents to bridge vocabulary gaps in semantic search.
Coordinates multiple specialized AI agents using architectural patterns like fan-out/fan-in, supervisors, and synthesis for complex task execution.
Implements production-ready tool-use patterns and structured output schemas for LLMs using the latest industry standards.
Decomposes complex multi-concept queries into independent searchable terms to optimize retrieval accuracy and coverage in RAG pipelines.
Builds complex AI workflows using decorator-based patterns for parallel execution, persistence, and human-in-the-loop interactions.
Optimizes LLM performance and reduces API costs by implementing Redis-powered semantic similarity caching.
Scroll for more results...