lululemon

www.lululemon.com

Last scanned Jun 20, 2026, 6:14 AM

Verdict

Open to AI agents

ARC Score v1.0

29/100Restricted

How it's computed →

Agent access27.8/50

Structured data0/25

Protocol files0/15

Scan stability1.1/10

Agent access

Agent	Status	Source
GPTBot	Unknown	inconclusive
ChatGPT-User	Unknown	inconclusive
ClaudeBot	Unknown	inconclusive
Claude-Web	Allowed	robots.txt: no rule (default allow)
PerplexityBot	Unknown	inconclusive
Google-Extended	Unknown	inconclusive
Amazonbot	Unknown	inconclusive
Bingbot	Unknown	inconclusive
CCBot	Unknown	inconclusive

Infrastructure

PlatformcustomCDNakamaiWAFakamai

Data signals

JSON-LDNot detected
Schema.org ProductNot detected
Open GraphNo
Product feedNo
llms.txtNo

Change history · last 90 days

0 changes

No confirmed changes in the last 90 days — this brand's agent access posture has been stable.

The free public window is 90 days. Multi-year history, watchlists, and change alerts are part of Pro.

Fix it with Claude Code

Each failed check below has a ready-made prompt tailored to this scan's findings. Run it in Claude Code from your site's repository.

✗No JSON-LD structured datashow prompt

My e-commerce site is lululemon.com. 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.

✗No Open Graph tagsshow prompt

My e-commerce site is lululemon.com. A scan found no Open Graph meta tags, so link previews and many AI agents see untitled, imageless pages.

In this repository:

1. Add og:title, og:description, og:image, og:url, and og:type to the base layout's <head>, with sensible site-wide defaults.
2. On product pages, override them per product: og:type "product", the product image as og:image (absolute URL, ≥1200×630 where available), and the live price in og:description.
3. Add twitter:card "summary_large_image" alongside.
4. Show me the diff and one rendered <head> for a real product page.

✗No sitemap.xmlshow prompt

My e-commerce site is lululemon.com. A scan could not find a sitemap.xml, so crawlers and AI agents have no reliable way to discover our product pages.

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://lululemon.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.

✗No llms.txtshow prompt

My e-commerce site is lululemon.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://lululemon.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://lululemon.com/llms.txt

✗No machine-readable product feedshow prompt

My e-commerce site is lululemon.com. 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.

Embed this score

HTML

<a href="https://www.arcreport.ai/brand/lululemon"><img src="https://www.arcreport.ai/badge/lululemon.svg" alt="ARC Score for lululemon" height="22"></a>

Markdown

[![ARC Score for lululemon](https://www.arcreport.ai/badge/lululemon.svg)](https://www.arcreport.ai/brand/lululemon)

Updates automatically with each daily scan.

Scanned daily via robots.txt parsing and live HTTP tests for 9 AI agents. Changes are confirmed across two consecutive scans before publishing. Read the full methodology →