Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.

Provides configuration patterns for LangChain4J vector stores in 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.

Provides patterns for enabling and operating the Spring Cache abstraction in Spring Boot. Use when implementing application-level caching for performance-sensitive workloads.

Provides 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.

Provides distributed transaction patterns using the Saga Pattern in Spring Boot microservices. Use when building microservices requiring transaction management across multiple services, handling compensating transactions, ensuring eventual consistency, or implementing choreography or orchestration-based sagas with Spring Boot, Kafka, or Axon Framework.

Provides integration patterns for LangChain4j with Spring Boot. Handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.

Provides patterns for unit testing service layer with Mockito. Validates business logic in isolation by mocking dependencies. Use when testing service behaviors and business logic without database or external services.

Validates a skill against DevKit standards (requirements, template, dependencies). Use when you need to verify a skill before publishing or after modifications.

Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.

Provides patterns to 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.

Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

Provides comprehensive NestJS framework patterns 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.

Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and @Query), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.

Provides 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.

Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.

Provides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.

Provides testing strategies for LangChain4j-powered applications. Handles mocking LLM responses, testing retrieval chains, and validating AI workflows. Use when testing AI-powered features reliably.

Provides AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.

Provides patterns for unit testing scheduled and async tasks using @Scheduled and @Async. Handles mocking task execution and timing. Use when validating asynchronous operations and scheduling behavior.

Provides patterns for unit testing Jakarta Bean Validation (@Valid, @NotNull, @Min, @Max, etc.) with custom validators and constraint violations. Validates logic without Spring context. Use when ensuring data integrity and validation rules are correct.