Docs Audit

Docs Audit ("Documentation quality · AI-ready") is the AEO Audit tab built specifically for technical documentation, API guides, and code examples: the kind of content coding agents and developer-focused AI tools rely on most, and which has its own set of requirements beyond general marketing/content pages.

What it's for

General-purpose SEO/AEO checks don't fully capture what makes documentation usable by AI models: whether code blocks are clean and properly labeled, whether API references are structured so an agent can parse parameters correctly, and whether explanations are self-contained enough to be extracted and cited without missing context. Docs Audit evaluates a documentation URL against exactly that criteria, the same underlying concern the Dev Intelligence module tracks from the traffic/citation side.

Running an audit

• URL field + Audit Docs: point it at your documentation (a live docs site or even a shared doc link).
• Download PDF: export the full result as a shareable report.

Docs Audit doesn't require a single-page URL: it discovers your documentation's other pages on its own, via llms.txt, your sitemap, or heuristic path probing if neither is available. A badge above the results shows which discovery method was used and how many pages it found.

Score summary

• Score / Grade: an overall score out of 100, mapped to a letter grade.
• Passed / Failed / Warnings / Manual: how the checks broke down. Manual checks are ones the tool can't fully verify automatically and flags for you to confirm by hand.

Section results

Results are grouped into labeled sections. Each section shows its own fail/warn/pass counts, a progress bar, and a percentage score. Use the All / Fail / Warn / Pass / Manual filter to jump straight to the sections that need attention, and expand a section (▼) to see its individual checks.

• AI & LLM Discoverability: whether crawlers and agents can find your docs at all, llms.txt, sitemap coverage, and similar signals. Why it matters: none of the other sections matter if an agent can't locate the page in the first place.
• Structure & Navigation: headings, sidebar/page-tree structure, and consistent organization. Why it matters: agents often need to jump between related pages (a concept page and its API reference, for example); a predictable structure makes that traversal reliable instead of guesswork.
• Content Completeness: whether a page covers a topic end to end rather than assuming missing context. Why it matters: an agent extracting one page usually can't see the rest of your site; a page that depends on unstated prerequisites will get misapplied.
• Content Quality: clarity and precision of explanations. Why it matters: ambiguous wording is more costly in docs than in general content, since an agent may execute code based on a literal misreading.
• Technical SEO & Crawlability: baseline crawlability fundamentals applied to your docs specifically. Why it matters: documentation sites often live on separate subdomains or platforms from the main site, which can accidentally miss the crawlability setup the rest of the site has.
• Internal Linking & Flow: whether related pages link to each other in a way that reflects how the docs are actually used. Why it matters: good internal linking helps an agent (or a crawler building citation context) find the specific page a general question should route to.
• Versioning & Maintenance: whether the docs indicate what version they apply to and how current they are. Why it matters: an agent generating code from stale, unversioned docs can confidently produce integration code that no longer matches your current API.
• Agent Access & Markdown: whether pages are available in a clean, agent-consumable format (for example, raw Markdown) rather than requiring JavaScript rendering. Why it matters: agents work far more reliably from plain text than from a rendered HTML page they have to reverse-engineer, the same reason a /llms.txt file exists at all.
• Agent Tooling & Stability: whether code examples, install commands, and API references are formatted consistently enough for an agent to extract and run reliably. Why it matters: inconsistent formatting is a common reason an otherwise-correct agent produces broken setup steps.