Design

Complete shadcn/ui component library guide including installation, configuration, and implementation of accessible React components. Use when setting up shadcn/ui, installing components, building forms with React Hook Form and Zod, customizing themes with Tailwind CSS, or implementing UI patterns like buttons, dialogs, dropdowns, tables, and complex form layouts.

Database migrations and Drizzle ORM guidelines for the vm0 project

Guidelines for writing robust CLI end-to-end tests using BATS

Student weekly assignment planner. Shows all due dates, submission status, grades, and peer reviews across all courses. Use when student says "what's due", "plan my week", "weekly check", or wants to organize their coursework.

Guidelines for writing conventional commit messages that follow project standards and trigger automated releases

Tailwind CSS v4 utility-first styling patterns including responsive design, dark mode, and custom configuration. Use when styling with Tailwind, adding utility classes, configuring Tailwind, setting up dark mode, or customizing the theme.

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.

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.

shadcn/ui component library patterns with Radix UI primitives and Tailwind CSS. Use when creating tables, forms, dialogs, cards, buttons, or any UI component using shadcn/ui, installing shadcn components, or styling with shadcn patterns.

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.

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.

Guidance for using Forgetful semantic memory effectively. Applies Zettelkasten atomic memory principles. Use when deciding whether to query or create memories, structuring memory content, or understanding memory importance scoring.

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.

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.

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.

Implement distributed transactions 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.

Comprehensive testing patterns for Spring Boot applications including unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when implementing robust test suites for Spring Boot 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.

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.

Core Node.js backend patterns for TypeScript applications including async/await error handling, middleware concepts, configuration management, testing strategies, and layered architecture principles. Use when building Node.js backend services, APIs, or microservices.