---
title: "llms.txt audit | SEO Skill"
description: "Check whether llms.txt can be fetched and parsed, whether its links work and how well it covers useful pages before you publish or update it."
canonical: "https://seoskill.dev/docs/reports/llms-txt-audit"
language: "en"
---

# llms.txt audit

Audit an optional llms.txt file after you choose to publish one. The result checks the file and its linked pages without pretending that Google requires it or that it guarantees AI visibility.

## Install and run the audit

Install the CLI once, then run `seo start` to connect your site and Google data. After setup you can run this audit yourself or ask your agent to run it.

Install the SEO CLI globally.

```
npm i -g seo
```

Run setup after installation.

```
seo start
```

Check an optional llms.txt file and its linked pages.

```
seo llms audit --project example
```

## What you get from this audit

Check whether llms.txt can be fetched and parsed, whether its links work and how well it covers useful pages before you publish or update it.

Best for

A publisher has chosen to maintain llms.txt or wants to assess the optional format.

Needs

Saved or fresh crawl report

You get

Check whether llms.txt can be fetched and parsed, whether its links work and how well it covers useful pages before you publish or update it.

Use next

Create an llms.txt draft

Every report follows the same rules for missing data and outside estimates.[See how report data works](https://seoskill.dev/docs/reports#how-report-data-works).

## What you need before you run it

The report uses the information below. Optional sources are only used when you ask for them.

### Saved or fresh crawl report

Provides the observed llms.txt response and candidate source pages.

### Current Google AI feature guidance

Keeps optional file observations separate from Google crawl and indexing requirements.

## Use a different report for these jobs

These jobs need a different report or a manual review.

[

### You only need to check the Markdown shape of a draft or local file.

Use the browser validator for line-specific format and link-list checks without installing anything. It does not fetch the file or its linked pages.

Free llms.txt validator

](https://seoskill.dev/tools/llms-txt-validator)[

### You need to check the site’s wider crawl, indexability, snippet, and page-structure controls for AI search.

Run AI readiness on the crawl. It checks the controls and page evidence that apply beyond the optional llms.txt file, while still avoiding unsupported visibility or citation predictions.

AI search readiness audit

](https://seoskill.dev/docs/reports/ai-readiness)

### You need to know whether llms.txt improved AI citations, rankings, or visibility.

No automated report can attribute those outcomes to llms.txt. Keep a dated record of the change, review referral and visibility evidence over time, and treat any movement as observational rather than proof that the file caused it.

Needs more evidence

## Use the audit with an agent or in code

The command above is enough for most work. These options let an agent, script or application run the same audit with explicit input and structured output.

### CLI

Use the report catalog when an agent, script or CI job needs predictable JSON instead of the normal terminal output. Check the current input first, then run the audit with the validated parameters.

Check the current input before the agent or script runs the audit.

```
seo reports describe llms-txt-audit --json
```

Once the input is valid, run the audit through the report catalog.

```
seo reports run llms-txt-audit --params '{"reportId":"crawl_example_20260710"}' --json
```

### MCP

An MCP agent follows the same two-step flow. It inspects the current schema, fills the required fields and only then runs the audit.

Ask the MCP server for the current audit input.

```
{
  "id": "llms-txt-audit"
}
```

Run the audit after the agent has checked and filled the input.

```
{
  "id": "llms-txt-audit",
  "params": {
    "reportId": "crawl_example_20260710"
  }
}
```

### TypeScript

Install the package in a Node 22 project when your application needs to run the audit directly. The result includes the same evidence, warnings and limits returned through the CLI and MCP server.

Add the SEO package to your project.

```
npm install seo
```

Run the audit from your TypeScript application.

```
import { executeReport } from 'seo/mcp'

const result = await executeReport(
  'llms-txt-audit',
  {
  "reportId": "crawl_example_20260710"
},
)

console.log(result)
```

## What to do next

Fix parse errors, broken links and unintended omissions in the published file, then run the audit again. Keep the result as a file-quality check because it cannot show whether an AI product used the file.

1.  01Generate a draft only if someone will own it.
2.  02Keep normal crawl, index, and snippet controls correct.

## Related reports

[

Generate an llms.txt draft from useful crawled pages, then review the selected links and wording against the saved crawl before publishing the file.

llms.txt generator

](https://seoskill.dev/docs/reports/generate-llms-txt)[

Check whether crawlers can access and use important pages in AI search. Find technical blockers without inventing a visibility or citation score.

AI search readiness audit

](https://seoskill.dev/docs/reports/ai-readiness)[

Check the crawl, index, canonical and snippet controls used for Google AI search eligibility without predicting whether a page will be selected.

Google AI search readiness audit

](https://seoskill.dev/docs/reports/geo-gaps)

## Sources used by this audit

These primary sources define the provider data or search controls used by the result.

-   [Google guidance for AI features and websites](https://developers.google.com/search/docs/appearance/ai-features)