patterns▌

Complete Retrieval-Augmented Generation systems with LangChain4j for knowledge-enhanced AI applications. \n \n Document ingestion pipelines with configurable chunking, metadata management, and embedding generation using OpenAI or custom embedding models \n Vector search and content retrieval with filtering, re-ranking, and configurable similarity thresholds for precise context matching \n RAG-enabled AI services that automatically inject retrieved context into chat models, with support for multi

Delegate specialized tasks to isolated AI assistants with custom tools, models, and system prompts. \n \n Sub-agents preserve main context by isolating verbose tool outputs and intermediate reasoning, enabling longer sessions and cleaner conversations \n Three built-in agents available: Explore (Haiku, read-only codebase search), Plan (Sonnet, plan-mode research), and General-Purpose (Sonnet, full read/write access) \n Create custom agents in .claude/agents/ with YAML frontmatter and markdown pr

Event-Driven Architecture patterns for Spring Boot using domain events, transactional listeners, and Kafka messaging. \n \n Covers domain event design, ApplicationEventPublisher integration, and @TransactionalEventListener configuration for local event handling with AFTER_COMMIT phase guarantees \n Supports distributed messaging via Kafka and Spring Cloud Stream for inter-service communication with functional consumer beans \n Implements the transactional outbox pattern for reliable event publis

Similarity Search Patterns \n Patterns for implementing efficient similarity search in production systems. \n Use this skill when \n \n Building semantic search systems \n Implementing RAG retrieval \n Creating recommendation engines \n Optimizing search latency \n Scaling to millions of vectors \n Combining semantic and keyword search \n \n Do not use this skill when \n \n The task is unrelated to similarity search patterns \n You need a different domain or tool outside this scope \n \n Instruc

Feature-aligned CRUD services for Spring Boot 3 with layered architecture and Spring Data JPA. \n \n Establishes feature packages with domain, application, presentation, and infrastructure layers to maintain architectural boundaries and DDD principles. \n Covers complete CRUD workflows: entity modeling with invariants, repository interfaces, JPA adapters, transactional services, DTO records, and REST controllers with proper HTTP status codes. \n Includes validation patterns using jakarta.validat

Build MCP servers with Spring AI using declarative tools, prompt templates, and native Spring integration patterns. \n \n Exposes Spring components as AI-callable tools via @Tool annotation, with parameter documentation through @ToolParam for AI model understanding \n Supports three transport modes (stdio, HTTP, SSE) with built-in Spring Security integration for role-based access control and audit logging \n Includes reusable prompt templates using @PromptTemplate , dynamic tool registration, mu

Principle-based design patterns for website sections that avoid AI-generated aesthetics. \n \n Covers five core section types: heroes, cards, CTAs, trust signals, and testimonials with context-specific guidance for different business types \n Teaches WHY and WHEN to use each pattern, not just templates; includes explicit anti-patterns like democratic design, perfect symmetry, and generic copy \n Provides cross-cutting principles on hierarchy, asymmetry, and restraint that apply across all patter

Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.

Design patterns for building autonomous coding agents with tool integration, permissions, and human oversight. \n \n Core agent loop architecture with think-decide-act-observe cycle; supports multi-model selection for different task types \n Tool design patterns including file operations, code understanding, terminal execution, and browser automation with schema-based definitions \n Permission system with four levels (auto, ask-once, ask-each, never) and risk assessment for approval workflows \n

Production-ready integration patterns for Mapbox GL JS across React, Vue, Svelte, Angular, and vanilla JavaScript. \n \n Covers framework-specific lifecycle management (useEffect/useRef in React, mounted/unmounted in Vue, onMount/onDestroy in Svelte, ngOnInit/ngOnDestroy in Angular) with proper cleanup to prevent memory leaks \n Includes Web Components pattern for framework-agnostic reusable map elements and cross-framework compatibility \n Provides token management via environment variables, Se