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Found 134 Skills
Complex migration strategies for LangChain applications. Use when migrating from legacy LLM frameworks, refactoring large codebases, or implementing phased migration approaches. Trigger with phrases like "langchain migration strategy", "migrate to langchain", "langchain refactor", "legacy LLM migration", "langchain transition".
Integrate PICA into a LangChain/LangGraph Python application via MCP. Use when adding PICA tools to a LangChain agent, setting up PICA MCP with LangChain, or when the user mentions PICA with LangChain or LangGraph.
Use when adding LangChain-based LLM routes or services in Python or Next.js stacks; pair with architect-stack-selector.
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
Provides tool and function calling patterns with LangChain4j. Handles defining tools, function calls, and LLM agent integration. Use when building agentic applications that interact with tools.
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
AI agent with retrieval tool for document Q&A using RAG and LangGraph.
Apply production-ready LangChain SDK patterns for chains, agents, and memory. Use when implementing LangChain integrations, refactoring code, or establishing team coding standards for LangChain applications. Trigger with phrases like "langchain SDK patterns", "langchain best practices", "langchain code patterns", "idiomatic langchain", "langchain architecture".
Use when a migration is already known to stay on the LangChain agent side, including agent setup, tools, structured output, retrieval, and short-term memory.
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
LangChain / LangGraph engineering pitfalls and verified fixes. Covers DeepAgents, OpenAI-compatible model integration (including Chinese provider adapters: DeepSeek, Qwen, GLM, etc.), middleware, streaming, multi-agent orchestration, and other common development issues. Use when hitting unexpected behavior, making architecture decisions, or integrating Chinese LLM providers during LangChain development.