llmstxt

Fetch llms.txt and markdown fallbacks to standardize website documentation context.

3|1|Updated Apr 12, 2026
One-click install
npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill llmstxt-cogni-ai-ou
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: llmstxt
Source: https://github.com/Cogni-AI-OU/cogni-ai-agent-skills/tree/main/llmstxt
Command: npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill llmstxt-cogni-ai-ou

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

llmstxt standardizes how an agent discovers and uses LLM-friendly documentation context for any website by defining how to find and interpret llms.txt and related context files.

Core Features & Use Cases

  • LLM-first website context discovery: Fetches https://<domain>/llms.txt before relying on HTML or ad-hoc page scraping.
  • Expanded context support: Uses /llms-ctx.txt and /llms-ctx-full.txt when available to reduce fragmentation and token waste.
  • Markdown fallbacks for pages: Attempts .md renderings such as page.html.md or index.html.md to obtain cleaner input.
  • Authoring guidance for /llms.txt: Ensures required structure (H1, summary blockquote, optional file lists) so other agents can reliably load context.
  • Use case example: When documenting a codebase hosted at example.com, first load its llms.txt, then use the provided file lists and linked markdown pages to answer technical questions with consistent, curated context.

Quick Start

Use the llmstxt skill to fetch and apply https://<domain>/llms.txt (and any available llms-ctx*.txt) before retrieving specific site pages for your agent’s next inference step.

Frequently Asked Questions about llmstxt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is an llms.txt file and how does it provide LLM context for web documentation?

An llms.txt file provides LLM context for web documentation by standardizing how agents discover and fetch LLM-ready information from a domain. Agents retrieve this file before parsing HTML to obtain curated, markdown-friendly context and reduce token waste.

How do I make my website documentation LLM-friendly using markdown?

To make website documentation LLM-friendly using markdown, author an llms.txt file at your domain root with an H1, summary blockquote, and optional file lists. Agents fetch this context first, using linked markdown pages to minimize HTML parsing.

How do I fetch expanded context files to reduce token waste for LLM ingestion?

Fetch expanded context files by requesting /llms-ctx.txt or /llms-ctx-full.txt from the target domain. These consolidated files reduce token waste and fragmentation by providing larger blocks of LLM-ready context without needing multiple page fetches.

What is the best way to map HTML URLs to clean markdown endpoints for agents?

The best way to map HTML URLs to clean markdown endpoints is to implement .md fallbacks like page.html.md or index.html.md. Agents attempt these markdown renderings to obtain cleaner input and avoid parsing complex HTML structures.

When should I not rely on ad-hoc page scraping for LLM context discovery?

You should not rely on ad-hoc page scraping for LLM context discovery when a website provides standardized llms.txt or llms-ctx.txt files. Fetching these dedicated context files first ensures consistent, curated input and minimizes token waste.