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Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.
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Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.
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Build AI agents with LangChain v1 and LangGraph in Python. Use this skill whenever the user mentions LangChain, LangGraph, create_agent, init_chat_model, @tool, ToolRuntime, agent middleware, human-in-the-loop, structured output, MCP tools (langchain.mcp, MCPAdapter), RAG, multi-agent handoffs or subagents, checkpointers, Deep Agents (create_deep_agent) or Managed Deep Agents (mda, define_deep_agent). Also trigger for migrating from create_react_agent, LLMChain, AgentExecutor or langchain-mcp-adapters, stream_events, SummarizationMiddleware, and langchain-classic.
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Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.
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Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.
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Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
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Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
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エージェント、メモリ、ツール統合パターンを備えたLangChainフレームワークを使用してLLMアプリケーションを設計します。LangChainアプリケーションの構築、AIエージェントの実装、または複雑なLLMワークフローの作成時に使用します。
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使用 qingcloud CLI 操作青云 QingCloud。当用户提到青云、QingCloud、青云云、青云QingCloud 等时触发此技能。
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Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.
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Helps you build similarity search with FAISS to find nearby vectors, numerical representations of data. Covers index selection, saving and loading, GPU use, and tuning search speed, accuracy, and memory consumption for large datasets.
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Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.
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Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.
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Linting best practices and golangci-lint configuration for Golang projects — running linters, configuring .golangci.yml, suppressing warnings with nolint directives, interpreting lint output, and selecting linters. Use when configuring golangci-lint, asking about lint warnings or nolint suppressions, setting up code quality tooling, or choosing linters. Also use when the user mentions golangci-lint, go vet, staticcheck, or revive. Not for wiring a lint step into a GitHub Actions pipeline (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).
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INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.
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Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
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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.
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LangChain 1.0 使用指南。提供 Agent、Tool、Memory、Middleware 等核心概念的快速参考。当用户需要创建 AI Agent、集成 LangChain、或解决 LangChain 相关问题时激活。
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Go バックエンドの品質チェック（golangci-lint + Swagger 生成）。「lint」「golangci-lint」「静的解析」「swagger」「OpenAPI」「ドキュメント生成」などのキーワードで自動適用。
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Build search and retrieval systems with Pinecone, a managed database for numerical representations of content. Covers data uploads, filtered similarity searches, combined semantic and keyword search, and connections to LangChain and LlamaIndex.
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Build document search with Chroma, a database that stores text as numerical representations of meaning. Save and update documents, filter by metadata, and connect retrieval to AI applications using LangChain or LlamaIndex.
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Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.
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Implement, update, test, or document TypeScript provider packages, the adapters connecting supported services and frameworks. This skill focuses on work under ts/packages/providers and directs the agent to read the provider workflow before making changes.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
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