Analogue

My e-commerce site is analogue.co. A scan found no JSON-LD structured data at all, so AI shopping agents can't reliably read our product names, prices, availability, or images.

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 analogue.co. 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://analogue.co/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://analogue.co/llms.txt

My e-commerce site is analogue.co. A scan found no machine-readable product feed, which feed-based AI shopping agents (ChatGPT Shopping, Klarna, Google AI Mode) rely on.

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.