Agent Readability Studio
Paste a website URL and switch between an agent-readability audit and a conversion plan that shows how to make the site easier for AI agents to read.
What this proves
Agent-readable websites need a concrete operating pattern: an audit surface, a conversion plan, markdown twins, crawler policy, and canonical answers an agent can trust.
How it works
What This Proves
Websites have a second reader now.
Humans still need the normal visual page. Agents need a different surface: canonical text, stable route maps, markdown twins, explicit crawler rules, and pages that answer obvious questions without scraping a JavaScript app.
This build turns that idea into a product surface. It has two modes:
- Audit - score whether a site is readable by agents.
- Convert - generate the first pass of the machine-readable version.
The demo is intentionally client-side and deterministic. It does not crawl the submitted URL yet. The point of this version is to make the workflow shape obvious before turning it into a live scanner.
What I Built
The tool accepts any website URL and produces two linked views.
The audit view scores:
/llms.txtcoverage- markdown twins for important routes
- robots policy for AI crawlers
- sitemap and structured data
- server-rendered page structure
- canonical answerability
The conversion view turns the same URL into:
- a draft
/llms.txt - a markdown route plan
- robots.txt rules for GPTBot, ClaudeBot, and PerplexityBot
- structured content fixes
- before/after examples of what an agent can answer
If no URL is entered, the build opens with two examples: a healthy agent-readable surface and a generic SaaS site that needs conversion.
Why It Fits ShipWithTez
This is not generic AI SEO.
The interesting workflow is operational: when an agent lands on your site, can it answer pricing, scope, contact, changelog, docs, and integration questions without guessing?
That is a useful SWT proof because it turns a fuzzy trend into a concrete build pattern. It also creates a natural paid diagnostic: "I can make your website readable to agents."
What I Would Add Next
- Run a real fetch against
/llms.txt,/robots.txt,/sitemap.xml, and a few canonical pages. - Add screenshot and HTML extraction from the existing Landing Page Critic browser pipeline.
- Generate a downloadable patch for Next.js, Astro, and static sites.
- Store shareable result pages so an audit can become a social proof artifact.
- Measure referrals from ChatGPT, Perplexity, Claude, and other AI surfaces after deployment.
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