AI-Legibility Scan
A free, open-source CLI that scores how readable your site is to AI agents and language models — not how it looks to a human, but whether a model can parse who you are, what you do, and describe you correctly. One command, a 0–100 score, and a concrete fix list.
Why legibility?
More buyers now ask a model about you than read your homepage. The model reads the web on their behalf — and most of what wins is invisible to it: content that only renders after JavaScript, pages with no structured data, inconsistent descriptions that leave the model guessing. If a model can’t read you cleanly, it either skips you or describes you wrong.
The scan measures the signals that decide how a model sees you, and tells you exactly what to add.
What it looks at
- Crawlability without JS. Does meaningful content render in raw HTML, or only after JavaScript an LLM crawler won’t run?
- Structured data. schema.org / JSON-LD that states who you are and what you offer, machine-readably.
- Semantic structure. Real headings, landmarks, and question-shaped sections an answer engine can lift.
- Canonical description. One consistent paragraph about you, repeated across surfaces — the ranking signal.
- Metadata & canonicals. Titles, descriptions, canonical URLs, Open Graph — clean per-page identity.
- Agent-facing files. llms.txt, robots, and sitemap that tell models what to read and how to treat it.
We run it on ourselves
We hold our own site to it: a passing score is the fastest proof we’ve done the work. We target ≥80 on the homepage and ≥70 on our identity pages, and ship structured data, an llms.txt, and JS-free content to get there. The playbook behind those fixes is The Prove-It Protocol.
Run it on your own site and see what a model sees.
Part of the agent-readiness toolkit
Two open-source scanners, two questions every site should be able to answer:
- ai-legibility-scan — can LLMs read you? Structure, metadata, and machine-readability. GitHub
- agent-readiness-scan — can agents act on you? Discovery, trust boundaries, tools, and commerce. Read the intro
Open-source (run, read, contribute): github.com/KirkeLabs/ai-legibility-scan · npm