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agent_cli_langchain
agent_cli_langchain contiene 9 skills recopiladas de cwijayasundara, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Use when editing a DeepAgents project — adding tools, sub-agents, modifying the system prompt, choosing a filesystem backend, or composing extra middlewares (retries, fallbacks, HITL) on top.
Use when creating a new LangChain / LangGraph / DeepAgents project from scratch. Picks the right scaffolder for graphs vs. DeepAgents vs. LCEL chains.
Use when starting work on any LangChain / LangGraph / DeepAgents project. Entry point for the develop -> middleware -> evaluate -> deploy lifecycle, mapping each step to the right official tool.
Use when productionising or deploying a LangChain / LangGraph / DeepAgents agent. Covers durable execution (checkpointers, thread_id), the production middleware stack, three deploy targets (LangSmith Cloud, Cloud Run, Docker), secrets, scaling, and post-deploy verification.
Use when editing a non-agentic LCEL pipeline — composing Runnables, retrievers, embeddings, chat models, parsers, or building a RAG chain. For agents (LLM + tools loop), use the middleware skill instead.
Use when building a custom-graph LangGraph agent — when `create_agent(...)` + middleware isn't enough and you need explicit StateGraph control (multi-graph workflows, custom routing, non-standard state schemas, parallel branches). For the common case, use `create_agent` first (see middleware skill).
Use when authoring eval datasets, writing evaluators, running evals against a LangChain / LangGraph / DeepAgents project, comparing eval results between runs, or writing unit/integration tests for an agent.
Use when building or productionising any agent — adding retries, fallbacks, summarization, human-in-the-loop, PII redaction, call limits, or custom hooks. Middleware is THE composition primitive for modern LangChain agents (v1+); covers built-ins plus the custom middleware authoring API.
Use when debugging an agent's behaviour, reading LangSmith traces, setting up tracing in production (LangSmith + OpenTelemetry), wiring distributed tracing across services, or diagnosing common failure modes.