langgraph-docs

Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

25 stars

Best use case

langgraph-docs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

Teams using langgraph-docs should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/langgraph-docs/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/aiskillstore/marketplace/langchain-ai/langgraph-docs/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/langgraph-docs/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How langgraph-docs Compares

Feature / Agentlanggraph-docsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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.

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