Goodyear

My e-commerce site is goodyear.com. Our robots.txt does not block these AI agents, but live HTTP tests show our WAF/CDN returns 403s or challenge pages to them:

- Bingbot (Microsoft — Copilot / Bing)

This means our stated policy (allow) and our enforcement (block) disagree, and AI assistants silently fail on our store. In this repository and our infrastructure config:

1. Search for bot-management or firewall configuration (Cloudflare rules in code/terraform, vercel.json, middleware that filters user-agents, security headers config).
2. Where we control it in code, add allowlist entries for the user-agents above — scoped to product, category, and content pages only; keep protections on /cart, /checkout, /account, and admin routes.
3. Show me the diff, and flag any rule you find that blanket-blocks "bot-like" traffic.
4. If the blocking happens in a dashboard we don't keep in code (e.g. Cloudflare Super Bot Fight Mode, DataDome, PerimeterX), tell me the exact product setting to change and what to set it to.

After deploy, verify with: curl -A "GPTBot/1.0" -I https://goodyear.com/ (expect 200, not 403).

My e-commerce site is goodyear.com. A scan found JSON-LD on the site but no Schema.org Product markup, so AI shopping agents can't reliably read our product names, prices, availability, or images. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Find the product page template/component and add a JSON-LD <script type="application/ld+json"> block with Schema.org Product markup: name, description, image, sku, brand, and an Offer with price, priceCurrency, availability (use schema.org/InStock | OutOfStock), and url. Populate every field from our real product data — no placeholders.
2. Add an Organization JSON-LD block to the base layout (name, url, logo) if missing.
3. If we have category/listing pages, add ItemList markup referencing the product URLs.
4. Show me one fully rendered example of the JSON-LD for a real product, then validate the shape against Google's Rich Results requirements for Product and fix any warnings you can detect statically.

Verify after deploy with https://search.google.com/test/rich-results on a product URL.

My e-commerce site is goodyear.com. A scan could not find a sitemap.xml, so crawlers and AI agents have no reliable way to discover our product pages. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Generate a sitemap.xml covering the homepage, category pages, and every product page, with <lastmod> from each product's updated-at where available.
2. If the catalog is large, split into a sitemap index with child sitemaps of ≤50,000 URLs.
3. Reference the sitemap from robots.txt ("Sitemap: https://goodyear.com/sitemap.xml").
4. Make it regenerate automatically (build step or on-demand route) rather than a one-off static file, and show me the diff.

My e-commerce site is goodyear.com. We don't publish an llms.txt file yet — the emerging convention (llmstxt.org) that gives language models a concise, curated guide to a site.

In this repository:

1. Create /llms.txt (served at https://goodyear.com/llms.txt as text/plain or text/markdown) following the llms.txt format:
   - H1 with our brand name,
   - a one-paragraph blockquote summary of what we sell and who we serve,
   - sections linking to our most useful pages for an AI agent: bestsellers/category pages, shipping & returns policy, size guides, FAQ/support, and store locator if any.
2. Write the summary from this repository's real content (README, about page, homepage copy) — keep it factual, no marketing superlatives.
3. Keep it under ~200 lines, every link absolute.
4. Show me the full file content and where you wired it to be served.

Verify after deploy: curl https://goodyear.com/llms.txt

My e-commerce site is goodyear.com. A scan found no machine-readable product feed, which feed-based AI shopping agents (ChatGPT Shopping, Klarna, Google AI Mode) rely on. The site runs on salesforce, so prefer that platform's idiomatic way of serving these files/markup (theme templates, app settings, or metafields) over hand-rolled middleware where it exists.

In this repository:

1. Determine where product data lives (database models, CMS, platform API) and add a product feed endpoint — Google Merchant–compatible XML (RSS 2.0 with the g: namespace) at /feeds/products.xml, or JSON if that's more idiomatic here.
2. Include per item: id, title, description, link, image_link, price with currency, availability, brand, and gtin/mpn when we have them.
3. Paginate or stream if the catalog is large; cache for ~1 hour.
4. Link the feed from robots.txt or a <link rel="alternate"> in the layout, show me the diff, and print the first two feed items rendered from real data.