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Plugin
llm-application-dev
LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.2
$
npx claudepluginhub sumeet138/qwen-code-agents --plugin llm-application-devPopularity
Stars
Med: 0·Avg: 274
Installs
Med: 0·Avg: 1
What's Inside
Slash Commands3
AI Assistant Development
/ai-assistant
Build AI assistant application with NLU, dialog management, and integrations
LangChain/LangGraph Agent Development Expert
/langchain-agent
Create LangGraph-based agent with modern patterns
Prompt Optimization
/prompt-optimize
Optimize prompts for production with CoT, few-shot, and constitutional AI patterns
Agents3
ai-engineer
/ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
prompt-engineer
/prompt-engineer
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.
vector-database-engineer
/vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.
Skills8
embedding-strategies
/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
/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
/langchain-architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
llm-evaluation
/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
/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.
Stats
Version2.0.5
LanguagePython
Stars1
MaintenanceGood
LicenseMIT
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Uses power tools
Uses Bash, Write, or Edit tools
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