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.
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.
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.
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.
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.
Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
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.
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.
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.
Build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Prevents silent failures and context loss in error handling. Use when writing try-catch blocks, designing error propagation, reviewing catch blocks, or implementing Result patterns.
This skill should be used when implementing fault tolerance and resilience patterns in Spring Boot applications using the Resilience4j library. Apply this skill to add circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback mechanisms to prevent cascading failures, handle transient errors, and manage external service dependencies gracefully in microservices architectures.
Implement Event-Driven Architecture (EDA) in Spring Boot using ApplicationEvent, @EventListener, and Kafka. Use for building loosely-coupled microservices with domain events, transactional event listeners, and distributed messaging patterns.
Four-phase debugging framework that finds root causes before proposing fixes. Use when investigating bugs, errors, unexpected behavior, failed tests, or when previous fixes haven't worked.
React component patterns for reusable, maintainable code
State management patterns - Zustand, Jotai, Context
Comprehensive Tailwind CSS utility-first styling patterns including responsive design, layout utilities, flexbox, grid, spacing, typography, colors, and modern CSS best practices. Use when styling React/Vue/Svelte components, building responsive layouts, implementing design systems, or optimizing CSS workflow.
Run verification commands before claiming work is complete or fixed. Use before asserting any task is done, bug is fixed, tests pass, or feature works.
