Implement REST API design standards and best practices for Spring Boot projects. Use when creating or reviewing REST endpoints, DTOs, error handling, pagination, security headers, HATEOAS and architecture 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.

Generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.

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.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Provide repeatable CRUD workflows for Spring Boot 3 services with Spring Data JPA and feature-focused architecture; apply when modeling aggregates, repositories, controllers, and DTOs for REST APIs.

Dependency injection workflow for Spring Boot projects covering constructor-first patterns, optional collaborator handling, bean selection, and validation practices.

Configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services.

Comprehensive React 19 patterns expert covering Server Components, Actions, use() hook, useOptimistic, useFormStatus, useFormState, React Compiler, concurrent features, Suspense, and modern TypeScript development. Proactively use for any React development, component architecture, state management, performance optimization, or when implementing React 19's latest features.

Comprehensive NestJS framework guide with Drizzle ORM integration. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM. Covers controllers, providers, modules, middleware, guards, interceptors, testing, microservices, GraphQL, and database patterns.

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.

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.

Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.

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.

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.

Unit tests for external REST APIs using WireMock to mock HTTP endpoints. Use when testing service integrations with external APIs.