Audits
Every audit the scanner runs, grouped by category. Each page says what the audit checks, when it applies to your site, how it's scored and how to fix it.
Retrievability
- Covering llms.txtWhether an llms.txt file covers the URL you scanned, at the root (/llms.txt) or at its path (/docs/llms.txt).
- Clean fetchWhether the file fetches cleanly: HTTP 200, no auth, at most one same-host redirect.
- Content typeWhether the file is served as text/plain or text/markdown.
- Encoding and parsingWhether the file is valid UTF-8 and parses cleanly as markdown.
- AI crawler accessWhether robots.txt lets the major AI crawlers fetch your llms.txt.
Spec
- Single H1Whether the file has exactly one H1, and it names your site or organization.
- Summary blockquoteWhether a blockquote summary follows directly after the H1.
- H2 sectionsWhether the body is organized into H2 sections that contain link lists.
- Link line formatWhether link lines follow the spec form - [name](url): description.
Curation
- Signal-to-noiseThe share of the file’s links that lead to answer-bearing pages.
- Link descriptionsHow many links carry a description, and whether each says what the page answers.
- OrderingWhether the most citation-worthy content comes first.
- Token budgetWhether the file fits an agent’s context budget, about 8,000 tokens.
- URL hygieneWhether link URLs are absolute, canonical, parameter-free and redirect-free.
- Curation, not a sitemapWhether the file curates your site rather than mirroring your sitemap.
- Freshness signalsWhether the file carries dates that agree with your sitemap and show current content.
- LLM-friendly linksThe share of the file’s links that point to LLM-friendly content: markdown or plain text rather than an HTML page.
Agent readiness
- Role and scopeWhether the file frames its role, its intended audience and its scope for agents.
- Data-freshness tiersWhether volatile facts are labelled with how fresh they are, and how to caveat them.
- Intent routingWhether the file routes each kind of user question to the right page.
- Journey flowsWhether multi-step user paths are written out as numbered flows.
- Machine surfaceWhether your API or MCP server is documented well enough for an agent to call it.
- Action endpointsWhether the file tells agents how users can book, quote, apply, buy or get in touch.
- FAQ blocksWhether the file includes question-and-answer blocks for common questions.
- Entity identityWhether the file states who you are: your legal or brand name, other names or identifiers, and the meaning of your terms.
- API error handlingWhether the API documentation covers errors, rate limits, auth and fallbacks.
Compliance
- Regulatory guardrailsWhether the file carries the guardrails of the industry playbook you declared, rule by rule.
- Prohibited claimsWhether the file tells agents which claims they must never make, for each prohibited claim in your declared playbook.
- Required disclosuresWhether the file tells agents to give the disclosures your declared playbook requires.
- Human handoffWhether the file tells agents when to hand the user to a person, and how to reach one.
- Disclaimer textWhether the file gives agents disclaimer text, word for word, to attach to answers.
- Citation and licensingWhether the file says how you’d like to be cited, and on what terms content may be used by AI systems.
Ecosystem
- Named AI crawlersWhether robots.txt names AI crawlers explicitly, rather than relying on a catch-all rule.
- Sitemap freshnessWhether sitemap.xml is present and its lastmod dates are plausible.
- Markdown versionsWhether the pages your llms.txt links to have clean markdown versions.
- JSON-LD agreementWhether your JSON-LD / schema.org markup agrees with what the file says about who you are.
- Canonical agreementWhether the URLs in your file match the pages’ rel=canonical URLs.
- Agent filesWhether the site publishes agent instruction files: AGENTS.md, CLAUDE.md or SKILL.md.
- Link relationsWhether your pages declare their markdown version and the llms.txt that covers them, with link relations.