Total 30,774 skills, AI & Machine Learning has 4970 skills
Showing 12 of 4970 skills
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
Model Context Protocol (MCP) server implementation patterns with Spring AI. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates using Spring's official AI framework.
Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
Amazon Bedrock patterns using AWS SDK for Java 2.x. Use when working with foundation models (listing, invoking), text generation, image generation, embeddings, streaming responses, or integrating generative AI with Spring Boot applications.
This skill should be used when creating, optimizing, or implementing advanced prompt patterns including few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system prompt design. It provides comprehensive frameworks for building production-ready prompts with measurable performance improvements.
Scientific research and analysis skills
This skill should be used when orchestrating multi-agent swarms using Claude Code's TeammateTool and Task system. It applies when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.
Elite AI/ML Senior Engineer with 20+ years experience. Transforms Claude into a world-class AI researcher and engineer capable of building production-grade ML systems, LLMs, transformers, and computer vision solutions. Use when: (1) Building ML/DL models from scratch or fine-tuning, (2) Designing neural network architectures, (3) Implementing LLMs, transformers, attention mechanisms, (4) Computer vision tasks (object detection, segmentation, GANs), (5) NLP tasks (NER, sentiment, embeddings), (6) MLOps and production deployment, (7) Data preprocessing and feature engineering, (8) Model optimization and debugging, (9) Clean code review for ML projects, (10) Choosing optimal libraries and frameworks. Triggers: "ML", "AI", "deep learning", "neural network", "transformer", "LLM", "computer vision", "NLP", "TensorFlow", "PyTorch", "sklearn", "train model", "fine-tune", "embedding", "CNN", "RNN", "LSTM", "attention", "GPT", "BERT", "diffusion", "GAN", "object detection", "segmentation".