Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
8.1
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0
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AI & LLM
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Excellent LangGraph skill with comprehensive coverage of core concepts. The description clearly explains when to use this skill (stateful agents, ReAct patterns, production-grade applications). Task knowledge is strong with three detailed, executable patterns covering basic agents, state management, and conditional routing - all with clear 'when to use' guidance. Structure is good with logical organization (capabilities, requirements, patterns, anti-patterns). The anti-patterns section is particularly valuable for preventing common mistakes. Novelty is moderate-to-high: while a CLI agent could theoretically build LangGraph applications, the framework's complexity around state reducers, graph construction, and proper edge routing makes this skill genuinely useful for reducing tokens and errors. Minor improvement areas: could add persistence/checkpointer examples (mentioned in description but not shown), and human-in-the-loop patterns. Overall, this is a production-ready skill that delivers on its promise of expert LangGraph implementation guidance.
davila7
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