Prep Site For Ai

Prep a Site for AI

AI search engines, chat assistants, and autonomous agents now read your site and answer for you. If your facts aren't machine-legible, two things happen: the AI skips you, or it guesses, and a guessed fact is a wrong fact attributed to your business. The job here is to make every claim a machine can read directly, trace to a source, and reproduce without inventing anything.

This is a narrower, deeper pass than a general quality audit. It cares about one axis: can a non-human reader extract correct, current facts about you? (For the broad pass, a11y, performance, responsive, etc., use [audit-website-quality](../audit-website-quality/SKILL.md).)

1. Make the content actually reachable

An AI crawler that can't see your content can't quote it.

• Server-render the facts. Critical content (hours, prices, address, product details) must be in the initial HTML, not painted in later by JavaScript. Many AI crawlers don't execute JS. View source: if the fact isn't there, it doesn't exist to them.
• Don't gate facts behind auth, cookie walls, or "load more" interactions for the parts you want quoted.
• Reachable robots.txt + XML sitemap. Don't block AI user-agents you want to be cited by; do decide deliberately which ones you allow.

2. Express facts in structured data

Prose is ambiguous; markup is not. Add JSON-LD for every entity type the page represents:

• Organization / LocalBusiness: name, address, phone, hours, geo, sameAs (social profiles).
• Product / Offer: price, availability, currency.
• FAQPage, Article, Event, Review: as applicable.

Every value in the JSON-LD must match the visible page. Structured data that contradicts the rendered text is worse than none: it reads as deceptive and gets discounted.

3. Make facts machine-readable in the markup too

• Hours, prices, addresses as text, never baked into an image. An AI can't read a JPEG of your menu.
• Use semantic time/address elements (`, `) so the value is unambiguous.
• One clear, declarative sentence per fact beats a clever paragraph. AI extracts atomic claims.

4. Add an llms.txt

Publish /llms.txt (and optionally /llms-full.txt): a plain-Markdown summary of what the site is, the key facts, and links to the canonical pages for each topic. It's the AI-era equivalent of a sitemap aimed at language models: a curated, unambiguous source you control, so the model reads your framing instead of reconstructing one.

5. Signal provenance and freshness

AI systems weight sources they can trust and date.

• Show when a fact was last updated (visible date + dateModified in structured data). Stale-looking pages get discounted.
• Attribute claims to a real author/source where it matters (Article.author, bylines).
• Make the canonical URL explicit so the AI cites the right page, not a duplicate.

6. Render correct previews

• Open Graph + Twitter card metadata so links shared into AI/chat surfaces render a real title, description, and image.
• Canonical URL and OG URL agree.

7. Ground every fact: anti-slop

This is the rule that protects you from amplifying your own mistakes: only publish facts that trace to a real source. Invented hours, hallucinated awards, or generic AI filler don't just read as slop. Once an AI ingests them, it confidently repeats them everywhere. A machine-legible site full of unverified claims is a hallucination amplifier. Verify the source of each fact before you make it easy to extract.

Output

Produce a checklist of what's present vs. missing per section above, the specific markup to add (with the actual JSON-LD / llms.txt content drafted), and a flag on any fact that's currently in an image, behind JavaScript, or unverifiable. End with the one highest-leverage fix.

The principle underneath

This mirrors a website compiler's rule that everything factual traces to a source and nothing is invented. If you're building a system that generates many sites, bake these as policies the generator must pass rather than a manual checklist. See [build-sites-as-specs](../../code-engineering/build-sites-as-specs/SKILL.md).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ariaxhan
• Source: ariaxhan/the-agent-library
• License: MIT

Install and usage instructions live in the source repository linked above.