From langchain-skills
Scaffolds a minimal local LangGraph agent in TypeScript following the official quickstart. Guides through model selection, setup, and running a math agent example.
How this skill is triggered — by the user, by Claude, or both
Slash command
/langchain-skills:langgraph-typescript-quickstartThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Follow the live docs — do not invent an alternate API from memory:
Follow the live docs — do not invent an alternate API from memory:
https://docs.langchain.com/oss/javascript/langgraph/quickstart
Fetch that page (Docs MCP or HTTP) and implement what it shows (calculator / math agent with the Graph API). Prefer the Graph API path over the Functional API unless the user asks otherwise. Skip graph visualization.
Apply these on top of the quickstart (they keep setup minimal and model-agnostic):
Ask which provider/model to use. Showcase that LangGraph works with any LangChain chat model. Suggested prompt:
Which model should this agent use? Pass a
provider:modelstring — e.g.openai:gpt-5.5,anthropic:claude-sonnet-5,google-genai:gemini-2.5-flash-lite. Default if you're unsure:anthropic:claude-sonnet-5.
The docs often hardcode Anthropic — replace with initChatModel("<MODEL>") (or equivalent) using their choice. If using Claude Sonnet 5+, omit temperature / top_p / top_k (unsupported).
Create a new directory (e.g. langgraph-agent/) and do all work there — do not pollute the open project.
Only secret: the provider API key in .env (gitignored). No LangSmith / Tavily unless they ask. Prefer they edit .env themselves — don't paste keys into chat.
Install packages from the quickstart plus the provider package for their model.
Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to langgraph-fundamentals for next steps. For a higher-level agent API, use LangChain createAgent instead.
npx claudepluginhub langchain-ai/langchain-skills --plugin langchain-skillsScaffolds a minimal local LangChain agent in TypeScript from the official quickstart. Asks for provider/model choice and sets up local environment with Node 22+.
Provides expertise in LangGraph for building stateful multi-actor AI applications with graph construction, state management, cycles, persistence, human-in-the-loop patterns, and ReAct agents.
Initializes a new LangChain TypeScript project with Bun/NPM package init, core dependencies like @langchain/core and @langchain/openai, tsconfig.json, example ReAct agent, .env.example, and scripts.