Analyze and Generate Schema Markup

Schema Markup Analysis and Generation inspects a page for structured data, validates the detected markup and drafts suitable JSON-LD. It looks for embedded JSON-LD, Microdata attributes and RDFa attributes, then connects each detected structure to the page's actual content and required properties.

Validation checks missing context, invalid types, incorrect data types, placeholders, relative addresses and invalid date formats. It also checks whether a chosen type is supported for the intended search result feature. Structured data should describe truthful, verifiable facts rather than introduce unsupported claims about the organization, product, person or page.

Use it when structured-data detection, validation or generation is the main task, including questions about rich results and markup opportunities. It is narrower than a complete search audit. Select types from page-content analysis rather than adding every available type indiscriminately.

Schema.org vocabulary validity and Google rich-result eligibility are separate questions. A retired search feature does not automatically invalidate its vocabulary type. Check current official documentation for the relevant search surface before removing truthful markup. Historical status lists are reference snapshots rather than live eligibility guarantees, and valid markup alone is not a promise of a rich result.

Inputs include the page content or reachable page, existing structured data and verified business or editorial facts. Generation identifies the page type, selects appropriate structures and includes required and recommended properties. Unknown values remain clearly marked placeholders for the user to complete; unresolved placeholders must not be presented as deployable data.

The intended outputs are SCHEMA-REPORT.md for detection and validation results and generated-schema.json for proposed markup. The report identifies each detected structure, its type, status and issues, then lists missing opportunities and necessary fixes. Validate generated output before presenting it for implementation.

If the page is unreachable, report the connection error and check whether authentication is required. If nothing is detected, report that absence across all supported formats and recommend types based on actual content. Invalid JSON should produce specific syntax findings and corrected output rather than a generic failure message.

JSON-LD is the preferred primary format in this workflow. Markup can describe organization identity and contact information, local business addresses and opening hours, and article authorship, publication dates, images and publisher details. This exact example property is a fragment, not a complete document:

"@type": "Organization",

Other opportunities include products, offers, reviews, breadcrumbs, profiles, media, events and job postings when the page supports them. Restricted FAQ eligibility and historical retirements need current verification. For time-sensitive product or offer information, the guidance favors markup in initial server-rendered HTML because JavaScript-injected data can face processing delays.

The full schema-types reference and template collection are not bundled. Obtain the declared upstream material or an existing installation, inspect it and resolve paths before claiming those procedures or templates were used. Inline steps can still proceed independently within the user's authorized scope.

This skill suits site maintainers and search specialists who can verify page facts and review proposed markup. It produces an analysis and implementation draft, with current eligibility and final page validation still required.