Schema markup validator — what catches errors before launch

Structured data errors are silent. The page renders. The schema breaks. The validator's job is to find what the browser hides.

Most structured data errors are invisible to the person who built the page. The browser renders the content. The page loads. The schema is broken. An AI system reads the page, finds nothing to attribute, and moves on. A validator catches the problem before it costs citations.

What a schema markup validator does

A schema markup validator is a tool that checks whether a page's structured data is correct — syntactically valid, semantically complete, and machine-readable in the way AI systems and search crawlers expect.

Structured data does two jobs on a well-built page. First, it tells a crawler what type of page this is — a FAQ, a product, an organization, a how-to guide. Second, it populates the fields that AI systems use to attribute content to a brand: name, description, entity identity, relationships.

When the structured data is wrong, both jobs fail silently. The page is crawled. The schema is ignored. The citation opportunity disappears.

A validator's job is to surface that failure before the page ships — before the gap has cost a week of citation share.

Where structured data errors hide

Schema markup errors cluster in four places.

Missing JSON-LD entirely. The most common failure. A page type that should emit structured data emits nothing. No error in the browser. No warning in the CMS. The structured data was never written, was conditionally suppressed, or was lost in a theme update.

Malformed @type or @context. The JSON-LD block exists but fails to parse. Wrong @context URL. Misspelled @type value. The parser moves on. The schema is invisible to the reader that matters.

Missing entity identity. The structured data covers content but omits the identity block — Organization, Person, or LocalBusiness. AI systems can cite the content without knowing whose site they're on. Attribution fails.

FAQ schema in the wrong shape. FAQ markup in the wrong position. Question-Answer pairs that don't match the FAQ schema spec. The schema.org parser finds the block; the AI system cannot use it for question-level sourcing.

All four are common. None are visible in the browser. A validator that runs against source files — not just live pages — catches them at the authoring stage.

The three tools agencies use to validate schema markup

Google Rich Results Test. The standard tool for validating schema markup against Google's supported rich result types. Accepts a URL or a code snippet. Reports which rich result types are eligible, which are not, and what errors are blocking eligibility. Requires a live URL or a pasted HTML snippet. Cannot run against a content directory before deployment.

Schema.org Validator. Validates structured data against the full schema.org vocabulary. More permissive than the Rich Results Test — it checks whether the markup is schema.org-compliant, not whether it qualifies for a specific Google feature. Accepts a URL or a pasted snippet. Same constraint: requires a live URL or a manual paste. Does not run in CI.

ailk audit. Runs against source files. No deployed URL required. Reports four rules across every page in the content directory: schema coverage, JSON-LD validity, entity markup, FAQ and HowTo incentive. Exits 1 in CI when a passing rule regresses. The validation happens at the authoring stage, not the analytics stage.

The three tools are complementary. Google Rich Results Test verifies a live page's rich result eligibility. Schema.org Validator checks schema.org compliance for a snippet. ailk audit checks structural correctness at the page-file level across the full content directory, before any of it ships.

ailk audit — the validator that runs before launch

ailk audit is the schema validation tool built into AILK's OSS. It runs four rules against every page in the content directory.

Schema coverage. Every page type in the 45-entry AILK registry requires a schema. If a page is missing its JSON-LD, the rule fires. This catches the pages that look complete in the browser but emit nothing to the parser.

JSON-LD validity. Structured data that emits but fails to parse does nothing. The rule checks JSON-LD payloads against expected structure — malformed @type, missing @context, schema that compiles but breaks on the reader's end.

Entity markup. The audit checks whether site-identity schema — Organization, Person, or LocalBusiness — is present and structurally correct. This is the signal that tells an AI system whose site it is reading. Without it, a model can cite the content but not attribute it.

FAQ and HowTo incentive. FAQ schema earns disproportionate citation — the question-and-answer shape is how LLMs source answers. The rule checks whether pages with FAQ content are emitting the corresponding schema.

To run it:

ailk audit

Human-readable output by default. JSON for tooling:

ailk audit --format json | jq '.score'

In CI with regression detection:

ailk audit --format json > audit.json

next run

ailk audit --baseline audit.json

When a rule that was passing regresses, the run exits 1. The schema markup validator loop — write frontmatter, audit, fix, ship — closes at the authoring stage. Enterprise AEO platforms charge $295/month to measure the same problems after they have cost citations. ailk audit catches them before launch. Free.

Frequently asked questions

Can I use ailk audit to validate schema on a site not built with AILK?

No. The audit rules reference the AILK page-type registry. A non-AILK site will fail every schema coverage rule because the registry types are not present. For non-AILK sites, use Google Rich Results Test or Schema.org Validator against the live page.

How does ailk audit differ from Google Rich Results Test?

Google Rich Results Test validates a live URL or pasted HTML snippet against Google's rich result eligibility criteria. ailk audit validates source files across the full content directory before deployment, checking four structural rules. They serve different stages: Google Rich Results Test is a post-deploy check; ailk audit is a pre-deploy check.

Does the validator check backlinks, page speed, or crawl status?

No. ailk audit measures the structural layer — schema coverage, JSON-LD validity, entity markup, FAQ schema. It does not measure crawl status, page speed, backlink authority, or Google's current index state. Those belong to Search Console, GA4, and post-launch analytics.

Is ailk audit available on the free OSS tier?

Yes. The @ailk/aeo package ships in the OSS. Apache 2.0. No Pro license required to run it.

Validate your schema before it ships

ailk audit runs against your content directory and reports schema coverage, JSON-LD validity, entity markup, and FAQ incentive before a single page deploys. Free in the OSS.