The problem

Standard SEO targets human eyes, but AI research and shopping agents often fail to parse sites that lack machine-readable structure. If your site relies on PDFs, image-based text, or complex UI flows, agents like Perplexity or ChatGPT will skip your product or provide inaccurate data to users.

What it does

• Evaluates site discoverability by checking for llms.txt files and robots.txt configurations.
• Audits machine-readable structure including schema.org markup for products, offers, and FAQs.
• Tests transaction readiness for agentic commerce protocols like Stripe ACP or Shopify agent checkout.
• Generates a scored report (0-20) across five critical agent-readiness dimensions.
• Produces a ready-to-deploy llms.txt draft tailored to the specific business.

Why this beats prompting it yourself

This skill applies a consistent, weighted scoring framework specifically designed for the emerging field of Agentic SEO. It bypasses generic advice by enforcing a structured audit format that includes prioritized technical fixes and a pre-formatted llms.txt file.

Use cases

• Auditing a production e-commerce site to ensure AI shoppers can parse prices and stock.
• Pre-launch review of B2B SaaS landing pages to maximize LLM discovery.
• Converting marketing PDFs and image-heavy content into agent-readable plain text structures.
• Drafting standardized llms.txt files for existing web properties.

Known limitations

This is a reasoning-based audit based on provided text or fetched URLs, not a deep technical penetration test or a live multi-step transaction simulation.