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langgraph-docs skill

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This skill helps you locate and summarize LangGraph Python documentation to provide accurate, up-to-date guidance for implementation.

This is most likely a fork of the langgraph-docs skill from illuminated2020
npx playbooks add skill microck/ordinary-claude-skills --skill langgraph-docs

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: langgraph-docs
description: Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.
---

# langgraph-docs

## Overview

This skill explains how to access LangGraph Python documentation to help answer questions and guide implementation. 

## Instructions

### 1. Fetch the Documentation Index

Use the fetch_url tool to read the following URL:
https://docs.langchain.com/llms.txt

This provides a structured list of all available documentation with descriptions.

### 2. Select Relevant Documentation

Based on the question, identify 2-4 most relevant documentation URLs from the index. Prioritize:
- Specific how-to guides for implementation questions
- Core concept pages for understanding questions
- Tutorials for end-to-end examples
- Reference docs for API details

### 3. Fetch Selected Documentation

Use the fetch_url tool to read the selected documentation URLs. 

### 4. Provide Accurate Guidance

After reading the documentation, complete the users request.

Overview

This skill fetches and surfaces LangGraph / LangChain Python documentation to provide precise, up-to-date guidance for implementation and conceptual questions. It locates the documentation index, selects the most relevant pages, reads them, and synthesizes actionable answers or code examples. The goal is to reduce guesswork and point you to authoritative docs and snippets you can use immediately.

How this skill works

The skill first reads the documentation index to discover available pages and their descriptions. It then selects 2–4 pages that best match the user’s request (implementation guides, core concepts, tutorials, or API references), fetches those pages, and synthesizes their content into a concise, actionable response. When appropriate, it returns links and short code or configuration tips extracted from the docs.

When to use it

  • You need authoritative LangGraph/LangChain Python guidance for implementation or troubleshooting.
  • You want concrete code examples or configuration patterns from official docs.
  • You need to compare API behaviors or options across versions.
  • You want a quick tutorial-style summary for an end-to-end workflow.
  • You need citations or links to specific documentation pages to validate guidance.

Best practices

  • Start with a focused question to let the skill pick the most relevant docs (how-to, tutorial, or API reference).
  • Request specific outputs (code snippet, config, explanation, or link list) to get concise, actionable answers.
  • Ask for version or compatibility notes if you depend on a particular LangChain/LangGraph release.
  • Use the provided links to validate and test code in your environment before production deployment.
  • If behavior differs from the docs, include environment details so the skill can fetch troubleshooting pages.

Example use cases

  • Implementing a LangChain agent that routes calls through LangGraph and needing sample Python code.
  • Explaining the difference between core LangChain LLM classes and LangGraph integrations.
  • Finding a tutorial that demonstrates an end-to-end pipeline (prompt -> agent -> graph -> tools).
  • Looking up parameter meanings and defaults for a specific LangGraph client or component.
  • Verifying recommended deployment or configuration steps for LangGraph in a production setup.

FAQ

Can this skill provide exact code from the docs?

Yes — it can fetch and reproduce short code snippets from documentation and summarize longer examples into concise, runnable snippets.

How many documentation pages will it consult?

It selects and synthesizes content from 2–4 of the most relevant pages discovered in the documentation index to keep answers focused and authoritative.