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The one check almost every site fails, and why it matters

Pillar percentages hide which single check is doing the damage. Re-running the engine check by check across the same cohort surfaces it.

Published · updated · by , founder

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Which single check does almost every site in the cohort fail?

sd.article — "Article / TechArticle / Dataset for content pages." Re-running the ARB engine against the same 30-site cohort defined in cohort.json (2026-08-24), it failed on 30 of 30 sites (100%) and passed on none.

What exactly does this check look for?

A JSON-LD block on the page typed Article, TechArticle, BlogPosting, Dataset or Report. Per the published rubric, its weight is 2 of 18 points in the Structured Data pillar — small alone, but never earned across the cohort.

Why does almost every homepage fail this by default?

The cohort was measured at each site's root URL — an Organization or WebSite page, not a blog post. The check targets content pages; a homepage is not one unless it deliberately adds an Article-typed node of its own.

Which checks are the next most commonly failed?

FAQPage/QAPage markup and BreadcrumbList each fail on 96.7% of the cohort (29 of 30), tied with a stated AI/agent access policy. A named author or reviewer fails on 93.3% (28 of 30) — all recomputed the same way.

CheckTitleWeightFail ratePass/Partial/Fail
sd.articleArticle / TechArticle / Dataset for content pages2100.0%0/0/30
sd.faqFAQPage or QAPage markup396.7%1/0/29
sd.breadcrumbBreadcrumbList markup296.7%1/0/29
gv.ai_policyStated policy for AI/agent access396.7%1/0/29
ev.authorNamed author or reviewer393.3%2/0/28
ag.apiMachine endpoint or API discoverable380.0%6/0/24
ev.datedContent carries a machine-readable date473.3%8/0/22
ag.contact_ldContact details in structured data366.7%10/0/20
ex.question_headsQuestion-form headings456.7%3/10/17
sd.sameassameAs identity links in JSON-LD346.7%16/0/14

Source: re-ran the ARB engine (methodology at methodology.json) against the 30-site cohort in cohort.json/cohort_run.py. Recomputed 2026-08-24T23:09:21Z UTC.

Why does missing Article schema matter for AI answer engines specifically?

Article/TechArticle typing is one of the strongest signals an answer engine uses to decide a page is dated, authored, citable content rather than a navigation shell. Without it, an otherwise strong page is structurally invisible as a citable claim.

How was this measured, exactly?

By importing the unmodified ARB/1.0 engine and calling engine.audit() against each of the same 30 URLs cohort_run.py defines, then counting each check's verdict across all 30 runs — not inferred from the pillar percentages already published in cohort.json.

What the rubric itself says to do about it

The published fix text for sd.article is direct: “Wrap research and article pages in Article/TechArticle with datePublished and author.” — taken verbatim from methodology.json, not paraphrased. It names two things a content page needs that a homepage usually lacks: a datePublished, and a named author.

  • Identify which pages are actually content. A pricing page or a homepage is not an article; a guide, a report, or a dated explainer is. Apply the fix only where it is true.
  • Add the datePublished and dateModified. Both dates need to be real — the day the page went live, and the day it last substantively changed, not today's date copied onto every page indiscriminately.
  • Name a real author, with a bio URL. A generic "ApexClaw" organization author still satisfies a schema validator; it does not satisfy a reader, or an answer engine, asking who stands behind the claim.
  • Keep the Article-family type alongside whatever type is already there. Adding a second JSON-LD node does not require removing an existing WebPage, AboutPage or ContactPage node.
  • Don't stop at the check that fails. A page can add Article/TechArticle and still miss the datePublished or the author the check is really asking about; re-check that both properties are present, not only the top-level type.
  • Expect this to be the cheapest fix on the list. Unlike a booking endpoint or a contact API, adding one JSON-LD node to an existing page requires no new infrastructure at all.

Method note

cohort.json and methodology.json publish pillar-level percentages, not per-check pass/fail counts. The table above required re-running the same, unmodified engine against the same named 30-site cohort to get check-level detail — the cohort membership and the rubric are both drawn from the two authorized files; only the granularity is new, and it is dated 2026-08-24.

Standards behind Structured Data checks: schema.org Article/TechArticle types are cross-referenced in this rubric's Governance pillar against OWASP Agentic Top 10, NIST AI RMF, ISO/IEC 42001 and the EU AI Act.

By Julian Joseph, Founder, ApexClaw. Every figure on this page is recomputed at build time from a named source file and date — see the method notes above. Reviewed against the claims policy: sourced, first-party, or labelled.