Hackett London

www.hackett.com
Last scanned Jun 20, 2026, 6:27 AM
Verdict
Open to AI agents
ARC Score v1.0
64/100Mixed
How it's computed →
Agent access50/50
Structured data4/25
Protocol files0/15
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

PlatformcustomCDNcloudflareWAFnone

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 hackett.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 hackett.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 llms.txtshow prompt
My e-commerce site is hackett.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://hackett.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://hackett.com/llms.txt
✗No machine-readable product feedshow prompt
My e-commerce site is hackett.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

ARC Score badge for Hackett London
HTML
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Markdown
[![ARC Score for Hackett London](https://www.arcreport.ai/badge/hackett-london.svg)](https://www.arcreport.ai/brand/hackett-london)

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 →