Each pillar shows the points earned against the maximum that was actually observable. Where a signal could not be observed it is removed from both sides rather than scored as a failure.
8 pts not observable
| Identity | 5.8 / 6 | |
| Identifiers | 3.4 / 12 | |
| Attribute depth | 4.1 / 7 |
| Descriptive content | 3.1 / 6 | |
| Imagery | 1.9 / 5 | |
| Ratings and reviews | 0 / 6 | |
| Catalog consistency | 2 / 3 |
| Public pricing | 5 / 5 | |
| Stock visibility | 4 / 4 | |
| Shipping in markup | 3.6 / 4 | |
| Return policy in markup | 3.9 / 4 | |
| Un-gated access | 3 / 3 |
| Product markup | 6 / 6 | |
| Required-field completeness | 5 / 5 | |
| Recommended-field depth | 2.2 / 5 | |
| Variant expression | 1 / 4 |
| Crawler access to products | not observable | — |
| AI crawler stance | not observable | — |
| Product sitemap | 0 / 4 | |
| Agent surfaceplatform | 0 / 3 |
We read every sampled page twice — once as a plain crawler reading your published markup, once through an extractor. Where the two disagree, an agent is likely to disagree too.
nothing lost — your markup survives extraction
sku, brand
0 of 7 compared pages lost at least one identifier in extraction.
| robots.txt readable | no — not reachable by a standards-compliant client |
| AI crawler stance | no AI user-agents named (an absent rule permits the crawler) |
| Sitemap | not found |
| UCP profile | none at /.well-known/ucp — this is not evidence against adoption |
| llms.txt | none |
Verified by hand from category listings — products taken from the middle of the list, never featured or best-seller placements. Open any of them and check our arithmetic.
hasVariant an agent sees unrelated products.It is not an audit and not a judgement of the business — it is a 8-page sample taken on a stated date, scored on a published rubric with no model judgement in it. Nothing here predicts whether any assistant will recommend you; for most answer engines that is decided by a private merchant feed we cannot see. If a number here is wrong, tell us and it goes in the public corrections log.
This scorecard is a sample — eight product pages, drawn at random from a published seed. It tells you where GameStop stands, not which SKUs are costing you. We’ll run a real sample of the whole catalog through the same extractor and send back the fields that are missing, by product.
Everything above stays free either way — the rubric, the sampled pages, the dataset, and every other company’s scorecard.