llm-application-dev

Plugin llm-application-dev from marketplace claude-code-ccf-marketplace in ccf/claude-code-ccf-marketplace. Commands read from the repo's manifest — 1 month ago.

Skills (9)

• embedding-strategies

  Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

• hybrid-search-implementation

  Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

• langchain-architecture

  Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

• llm-evaluation

  Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

• prompt-engineering-patterns

  Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

• prompt-engineering

  Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

• rag-implementation

  Build Retrieval-Augmented Generation (RAG) systems for LLM 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.

• similarity-search-patterns

  Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

• vector-index-tuning

  Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

Slash commands (3)

• /llm-application-dev:ai-assistant
• /llm-application-dev:langchain-agent
• /llm-application-dev:prompt-optimize

Subagents (2)

• ai-engineer
• prompt-engineer

Similar plugins