llms.txt Presence

The llms.txt Presence parameter checks whether your site publishes a valid /llms.txt file: a markdown summary that tells AI systems what your site offers without crawling every page. friendly4AI scores it 100 when a well-formed file is found, 50 when the file exists but is incomplete, and 0 when no file is present at any expected path.

What does the llms.txt parameter measure?

/llms.txt is an emerging standard. The file is a short markdown summary of your site written for AI systems, and this parameter checks whether you publish one. The scanner tries /llms.txt first, then falls back to /llms.md and /.well-known/llms.txt. Once it finds a file, it validates three structural elements:

• a top-level # title
• a > description blockquote
• at least one ## section heading

The scanner also counts the sections and the markdown links inside the file and records those as supporting evidence.

Why does llms.txt matter for AI-readiness?

A well-formed llms.txt works as a curated front door for AI systems: rather than piecing your purpose together from scattered pages, an LLM can read one concise document that names your site, says what it does, and links to the pages that matter most, and it hands you control over that framing.

That is the theory. In practice, when we studied production scan data against AI visibility, llms.txt showed no measurable association with whether AI assistants surfaced a site — it is an emerging standard with early adoption, and the AI systems are not yet consistently rewarding it. So treat llms.txt as low-cost hygiene: it is cheap to publish and does no harm, but it is not a visibility lever. Under the v4.6 methodology this parameter carries a reduced weight to reflect that. It stays in the catalog as an experimental AI signal while the standard settles; the bigger levers for AI-readiness are cite-worthy, answer-first content and a machine-actionable API.

How is llms.txt Presence scored?

This experimental AI-Specific Signals parameter is graded across three tiers under the v4.5 methodology, and the scanner applies the published rubric exactly:

• Pass (100) — a file is fetched and valid: it has a # title, a > description, and at least one ## section.
• Partial (50) — a file is fetched but the markdown is incomplete, missing any of those three required elements.
• Fail (0) — no file is found at /llms.txt, /llms.md, or /.well-known/llms.txt, or the response is blank.

Section and link counts go into the report as evidence, but the pass gate is the three-element structure check.

How do I fix llms.txt issues?

• Create /llms.txt at your site root in markdown: a # Your Site Name title on the first line, a > One-line summary blockquote, and one or more ## Section headings.
• Link to your most important pages from inside the sections so AI systems can follow them to deeper content.
• Scoring partial? Check that all three required elements are present. A missing description blockquote or section heading is the usual culprit.
• Treat /llms.txt as the primary path. The /llms.md and /.well-known/llms.txt fallbacks exist for compatibility, but the root file is the convention.
• Read the llmstxt.org specification for the full format, then re-scan to confirm the file validates.

Related parameters

• AI Manifests Coverage
• AI Usage Policy
• Sitemap Availability