# llms.txt / AI Crawler Audit Generator

Generate an llms.txt file (llmstxt.org v2 format: H1, blockquote summary, H2 file lists) from a live site crawl or a pasted page inventory, and audit robots.txt policies for GPTBot, ClaudeBot, PerplexityBot and Bytespider using the RFC 9309 matching algorithm — with allow/block previews and ready-to-paste directives.

> Canonical page: https://elysiatools.com/en/tools/llms-txt-and-ai-crawler-audit

- **Category:** Network

- **Keywords:** llms.txt, robots.txt, ai crawler, gptbot, claudebot, perplexitybot, bytespider, geo

## Overview

The llms.txt and AI Crawler Audit Generator creates standardized llms.txt files (v2 specification) and evaluates robots.txt access rules for major AI crawlers including GPTBot, ClaudeBot, PerplexityBot, and Bytespider. Whether analyzing a live URL or an offline inventory, it uses the RFC 9309 matching algorithm to deliver precise crawler allow/block matrices, ready-to-paste directives, and structured markdown previews.

## Inputs

- **Site URL to crawl (live mode)** (text): https://example.com — homepage links, plus sitemap.xml fallback for JS-rendered sites (max 25 pages)
- **Or paste pages (offline: URL | title | description)** (textarea): https://example.com/docs/quickstart | Quickstart | Install and run your first job https://example.com/docs/api | API reference | Endpoint catalog
- **Or paste robots.txt (offline)** (textarea): User-agent: GPTBot Disallow: / User-agent: * Disallow: /private/
- **Site / project name (llms.txt H1)** (text): Example Platform
- **One-line summary (blockquote)** (text): Example Platform lets teams ship scheduled data jobs without infrastructure.
- **Extra notes section (optional prose)** (textarea): Anything an LLM should know before using these pages…
- **Section grouping** (select)

## When to use

- When preparing your documentation or web content for indexing by LLMs and generative search engines via a clean llms.txt file.
- When auditing existing robots.txt files to verify whether AI bots like GPTBot or ClaudeBot can access or are restricted from specific site paths.
- When migrating or launching a new site and needing automated section grouping for markdown-formatted page inventories.

## How it works

- Input your site URL for live crawling (up to 25 pages with sitemap fallback) or paste your page inventory with URL, title, and description delimiters.
- Provide optional robots.txt directives and configure your project name, summary blockquote, extra notes, and grouping mode (flat or by first path segment).
- The RFC 9309 parser evaluates crawler rules across your paths, generating an interactive audit matrix alongside a valid, copy-ready llms.txt file.

## Use cases

- Engineering teams structuring technical documentation directories for retrieval-augmented generation (RAG) and LLM indexing.
- Technical SEO specialists auditing crawl budgets and disallow policies across commercial AI crawlers.
- Content managers drafting llms.txt files to provide clean, summarized context to AI agents visiting their web properties.

## Frequently asked questions

### What standard does the generated llms.txt follow?

It follows the llmstxt.org v2 format, structuring content with an H1 title, a blockquote summary, optional notes prose, and organized H2 page lists.

### Which AI bots are evaluated in the robots.txt audit?

The audit specifically matches rules for GPTBot, ClaudeBot, PerplexityBot, and Bytespider, alongside standard wildcard fallback directives.

### How does the live crawler handle JavaScript-heavy sites?

The crawler gathers links directly from the homepage and automatically falls back to sitemap.xml to discover up to 25 pages.

### What is the difference between auto and flat grouping?

Auto grouping categorizes your page entries into H2 sections based on their first URL path segment (e.g., /docs/, /blog/), while flat grouping outputs everything under a single section.

### Can I audit robots.txt rules offline without a live website?

Yes. You can paste both your page inventory and your raw robots.txt content into the offline inputs to test rule matching instantly.

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