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LynxHub provides automated llms.txt and llms-full.txt files so AI models and developer tools can index and process the documentation site efficiently. The llms.txt standard is a machine-readable directory format designed specifically for Large Language Models (LLMs), similar to how sitemap.xml functions for search engine web crawlers.

Available endpoints

You can access LynxHub documentation index files at the following canonical URLs:

File formats explained

LynxHub documentation serves two distinct text files depending on your AI model’s context needs:

llms.txt

The llms.txt file acts as a structured directory. It lists all available documentation pages alongside their descriptions and direct Markdown links.
  • Contains the site title, summary description, and categorized links to every page.
  • Allows AI tools to quickly discover where specific LynxHub features or configuration settings are documented.
  • Keeps context size low while enabling targeted sub-fetches.

llms-full.txt

The llms-full.txt file concatenates the entire LynxHub documentation into a single Markdown document.
  • Contains complete documentation text, code examples, and page content.
  • Useful when you want to feed the entire documentation suite directly into an LLM context window in a single request.

Connect AI tools to llms.txt

You can use llms.txt with various AI coding assistants and LLM tools:
1

Provide the URL to your AI chat tool

Pass https://docs.lynxhub.app/llms.txt directly to web-connected AI tools such as ChatGPT, Claude, or custom LLM agents.
2

Prompt the agent to inspect the index

Ask the agent to review the index file to locate the specific LynxHub topic you need help with.
3

Ingest full content if needed

If your LLM context window supports large inputs, provide https://docs.lynxhub.app/llms-full.txt to give the model complete knowledge of LynxHub in one pass.

When to use llms.txt

Use llms.txt when you want a lightweight overview of all LynxHub documentation pages.

Use llms.txt when

  • You need to discover available documentation topics.
  • You want to retrieve specific pages on demand without consuming excessive context.
  • You are configuring custom RAG pipelines or web scrapers.

Use other methods when

  • You need interactive, real-time doc search inside your IDE (use MCP Server).
  • You want AI agents to execute structured LynxHub workflows (use skill.md).
Last modified on July 20, 2026