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← State of Agentic Commerce
Agentic Commerce Score · scanned 2026-07-23 · rubric v0.2

palaceskateboards.com

16/100
grade F
not yet agent-buyable
platform: shopify

Can an AI shopping agent buy from palaceskateboards.com?

As of 2026-07-23, no: palaceskateboards.com scores 16/100 (grade F) on the Agentic Commerce Score and does not meet the agent-buyable bar. The failing checks are: AI crawlers allowed (robots.txt), Machine-readable product catalog, Required fields (title/image/price/description), and 8 more.

38
Discover — can agents fetch + read the store? (30%)
0
Evaluate — can agents parse + trust the products? (45%)
20
Transact — can an agent complete a purchase? (25%)

Discover — can agents fetch + read the store?

✗ AI crawlers allowed (robots.txt)
robots.txt root-blocks 6 AI agents: GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, Google-Extended, Amazonbot.
! Homepage readable without JavaScript
Homepage exposes only ~46 visible words without JavaScript.
✓ Sitemap discoverable
Sitemap found.
! llms.txt present
No /llms.txt (emerging convention — counted as a soft gap, not a blocker).

Evaluate — can agents parse + trust the products?

✗ Machine-readable product catalog
No open product feed, and only 0/2 sampled product pages carry complete Product JSON-LD — agents have no reliable catalog surface.
✗ Required fields (title/image/price/description)
No feed; from 2 sampled product page(s), Product JSON-LD carries 0% of the four required fields on average.
✗ Product JSON-LD (Offer: price/currency/availability)
0/2 sampled product page(s) carry Product JSON-LD with a complete Offer (price + priceCurrency + availability); 0/2 carry any Product schema.
✗ Brand + product identifier (sku/gtin/mpn)
0/2 sampled product page(s) expose both brand and an identifier (sku/gtin/mpn); brand alone on 0/2.
✗ AggregateRating in Product JSON-LD
0/2 sampled product page(s) expose machine-readable review ratings.
✗ Product image alt text
0% of content images on sampled product page(s) carry non-empty alt text.
✗ Description depth (≥120 chars)
0/2 sampled product page(s) expose a quotable description (≥120 chars) in Product JSON-LD.

Transact — can an agent complete a purchase?

✓ Agentic-checkout rail (platform prerequisite)
Platform: shopify. Shopify asset/runtime markers in homepage HTML.
✗ Machine-readable price + availability
No feed; 0/2 sampled product page(s) expose price + availability via Offer schema.
✗ Per-variant stock readable
No feed and no per-variant availability exposed — an agent cannot tell which size/colour it can actually order.
✗ Shipping + returns policies (with terms)
Neither a shipping nor a returns policy could be found — agentic checkout surfaces these before completing a purchase.

Highest-impact fixes

  1. Unblock AI shopping crawlers in robots.txt (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, Google-Extended…) — blocked agents cannot see, cite, or sell your products.
  2. Fill the four required catalog fields on every product: title, image, price, description — a product missing one drops out of agent answers.
  3. Add complete Product JSON-LD on product pages: an Offer with price + priceCurrency + availability.
  4. Publish shipping and returns policies as real pages with concrete terms (state the return window in days) — agentic checkout surfaces merchant policies before completing a purchase.
  5. Publish brand plus a product identifier (sku / gtin / mpn) in Product JSON-LD — agents match, dedupe and price-compare products by identifier; without one your listing is an orphan.
  6. Expose machine-readable price AND availability per product (feed fields or Offer schema) — agents will not guess stock.

Is this your store?

This is a point-in-time scan of public signals (2026-07-23) — if you have shipped fixes since, re-run it in ten seconds: npx agentic-commerce-score palaceskateboards.com.

ACS covers whether agents can buy from you. The other half is whether AI assistants recommend you over competitors — run Arenza's free Agentic Visibility Score or write to hello@arenza.ai.

Think a check is wrong? Every result carries the evidence it was based on, and the scanner is open source — so a disputed score is a reproducible question, not an opinion. Mail hello@arenza.ai and we will re-scan, correct the published page if we got it wrong, and note the correction. Fixed the gap instead? Tell us and we will re-scan and update this page.