LLM & Agents

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.

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.

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.

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.

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

Installs Sanity agent rules directly into the local project. Use when setting up a new editor environment.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

Dispatch code-reviewer subagent to review implementation against plan or requirements before proceeding

Protocol for tracking files immediately after agent creation

Create high-quality MCP servers that enable LLMs to effectively interact with external services. Use when building MCP integrations for APIs or services in Python (FastMCP) or Node/TypeScript (MCP SDK).

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

Common delegation patterns for PM agent

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

Use multiple Claude agents to investigate and fix independent problems concurrently

Code file content generation guide. Use this skill when you need to create code files that can be previewed via `frago view`. Covers supported languages, theme selection, and best practices.

PDF file content generation guide. Use this skill when you need to understand how `frago view` previews PDF files. Covers rendering features and limitations.

JSON file content generation guide. Use this skill when you need to create JSON files that can be previewed via `frago view`. Covers formatted display and best practices.

Markdown document content generation guide. Use this skill when you need to create Markdown documents that can be previewed via `frago view`. Covers element support, Mermaid charts, and best practices.

Information about running tests, test coverage, and known testing issues in the Rails application. Use when the user asks about testing procedures, encounters test failures, wants to know about coverage, or needs to troubleshoot intermittent system test issues.