Great question. Since you already have the scraping covered, I’d focus your agent more on identifying technical weaknesses rather than giving general SEO advice. For Shopify product pages specifically, I’d have it check things like: • Canonical tags (Shopify duplication issues are common) • Title/meta duplication • Proper heading structure (H1/H2 usage) • Schema markup (Product, Offer, Reviews) • Internal linking depth • Thin or duplicate product descriptions • Variant URL issues • Page speed elements (heavy images, app bloat, JS load) Basically: crawlability, structure, duplication, and performance, those are the real technical levers. For output, it depends on your goal: • If this is client-facing → clean written report with priority levels. • If this is operational → structured data in Sheets/Airtable is better so you can sort, filter, and track fixes. Personally, I like structured output + a short executive summary. It scales better. On automation, yes. I’ve seen good results using Make/n8n to: • Flag underperforming product pages • Identify missing schema at scale • Push content improvements into a draft workflow • Monitor page speed changes over time For AI search, clarity and structure matter more than keyword stuffing. Clean schema, strong internal linking, and well-structured product content seem to help more than anything fancy. Curious if you’re building this as a service workflow or experimenting for your own store?

https://www.fashionnova.com