Nurx

www.nurx.com
Last scanned Jun 20, 2026, 6:16 AM
Verdict
Open to AI agents
ARC Score v1.0
84/100Mostly open
How it's computed →
Agent access50/50
Structured data15/25
Protocol files9/15
Scan stability10/10

Agent access

AgentStatusSource
GPTBotAllowedrobots.txt: Allow
ChatGPT-UserAllowedrobots.txt: Allow
ClaudeBotAllowedrobots.txt: Allow
Claude-WebAllowedrobots.txt: Allow
PerplexityBotAllowedrobots.txt: Allow
Google-ExtendedAllowedrobots.txt: Allow
AmazonbotAllowedrobots.txt: Allow
BingbotAllowedrobots.txt: Allow
CCBotAllowedrobots.txt: Allow

Infrastructure

PlatformwoocommerceCDNcloudflareWAFnone

Data signals

  • JSON-LDDetected
  • Schema.org ProductNot detected
  • Open GraphYes
  • Product feedNo
  • llms.txtYes

Change history · last 90 days

9 changes
  1. Jun 14, 2026
    Amazonbot robots.txtno_ruleallowed
    Bingbot robots.txtno_ruleallowed
    PerplexityBot robots.txtno_ruleallowed
    Google-Extended robots.txtno_ruleallowed
    CCBot robots.txtno_ruleallowed
    Claude-Web robots.txtno_ruleallowed
    ClaudeBot robots.txtno_ruleallowed
    GPTBot robots.txtno_ruleallowed
    ChatGPT-User robots.txtno_ruleallowed

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 Schema.org Product markupshow prompt
My e-commerce site is nurx.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 woocommerce, 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.
No machine-readable product feedshow prompt
My e-commerce site is nurx.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 woocommerce, 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.

Embed this score

ARC Score badge for Nurx
HTML
<a href="https://www.arcreport.ai/brand/nurx"><img src="https://www.arcreport.ai/badge/nurx.svg" alt="ARC Score for Nurx" height="22"></a>
Markdown
[![ARC Score for Nurx](https://www.arcreport.ai/badge/nurx.svg)](https://www.arcreport.ai/brand/nurx)

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 →