Your agent can do more. Find the skill that makes it happen.
Find your next superpower If you are an agent, refer to our llms.txt for full access.Generates document ingestion pipelines, embedding stores, vector search, and semantic search using LangChain4j. Supports document question answering, knowledge-base assistants, repository search, and source attribution.
Configures AI model beans, chat memory, and RAG pipelines within Spring Boot and Spring Data. Covers auto-configuration, dependency injection, and integration with the Spring ecosystem.
Provides unit and integration testing patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and uses Testcontainers for Java AI service integration tests.
Generates tool and function calling patterns using @Tool annotations, tool executors, and AiServices registration. Covers parameter validation, execution errors, tool responses, and external API integration in LLM applications.
Provides vector store configuration patterns for LangChain4j RAG applications. Covers PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, and Neo4j, including embedding storage and retrieval, hybrid search, and performance optimization.
Covers Langfuse tracing, prompt management, evaluations, and datasets for LLM applications. Includes integrations with LangChain, LlamaIndex, and OpenAI to support production debugging, monitoring, and improvement.
Supports querying or modifying Langfuse data and finding documentation, integration guides, and SDK usage. Covers AI observability, prompt management, datasets, evaluator management, experiments, feedback collection, and evaluation-driven CI/CD.
Covers LangGraph graph construction, state management, cycles, and branches for stateful AI applications. Includes persistence with checkpointers, human-in-the-loop workflows, and the ReAct agent pattern.
Covers LangGraph graph construction, state management, cycles, branches, and persistence with checkpointers. Includes human-in-the-loop workflows and the ReAct agent pattern.
Guides use of ChatGoogleGenerativeAI, LangChain's chat model wrapper for the Google Gemini series. Supports various applications, including file processing.
Covers the langgraph CLI commands new, dev, build, up, and deploy. Includes application configuration through langgraph.json.
Fetches and references LangGraph documentation to guide implementation of graph agents and state machines. Supports multi-agent workflows, agent orchestration, API use, and human-in-the-loop patterns.
Skills give your agent reusable instructions for a specific job. Pick one, read what it does, and bring it into your workflow.
A name only tells half the story. Search the full description and instructions to find the right fit.
Read the skill, visit its source, and see exactly what you’re adding to your agent.
Copy the install command from a skill page and run it in your project.
npx skillycli add owner/repo --skill name