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langchain

Skill1 starsChanged 6 months agoMITView on GitHub

Use this skill when the user asks to setup LangChain, create a chain, build a RAG application, configure an LLM provider (OpenAI, Anthropic, local models), create custom tools, or work with LangChain Expression Language (LCEL). Triggers include: "setup langchain", "create chain", "build RAG", "configure LLM provider", "LCEL", "langchain prompt", "langchain model", "retriever", "vector store".

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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Document loaders and text splitters

    line 49

“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

LangChain Skill

When to Use

  • "setup langchain"
  • "create chain"
  • "build RAG"
  • "configure LLM provider"
  • "langchain model"
  • "LCEL chain"
  • "langchain prompt template"
  • "retriever setup"
  • "vector store"
  • "custom tool"

What It Covers

  1. Install and configure LangChain (v0.3+)
    • Core packages: langchain, langchain-core, langchain-community
    • Provider-specific packages (e.g., langchain-openai, langchain-anthropic)
    • Environment variables for API keys
  2. Create chains with LCEL (LangChain Expression Language)
    • Pipe operator (|)
    • RunnablePassthrough, RunnableLambda, RunnableParallel
    • Composing complex pipelines declaratively
  3. Setup prompts and output parsers
    • ChatPromptTemplate and PromptTemplate
    • Structured output with Pydantic
    • StrOutputParser, JsonOutputParser
  4. Configure LLM providers (OpenAI, Anthropic, local)
    • init_chat_model for unified model initialization
    • Provider-specific configuration (temperature, max_tokens, base_url)
    • Local models via Ollama, llama.cpp, or vLLM
  5. Build RAG applications
    • Document loaders and text splitters
    • Vector stores (Chroma, FAISS, Qdrant)
    • Retrievers and retrieval chains
  6. Create custom tools
    • @tool decorator
    • Tool schemas with Pydantic
    • Binding tools to models

Templates

TemplatePurpose
templates/models.pyConfigure LLMs with init_chat_model
templates/prompts.pyReusable prompt templates
templates/chains.pyLCEL chain examples
templates/requirements.txtRequired dependencies

Examples

ExamplePurpose
examples/basic-chain.pySimple LCEL chain with prompt + model + parser

Resources

Use When

The user wants to build LLM-powered applications with LangChain, including chains, RAG pipelines, agents, or custom tools. Use for both quick scripts and production-grade setups.

Download now (ZIP)

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