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Grok Bot+18 agentsShip Schema That Wins Rich Results: JSON-LD Audit
Schema markup audit for JSON-LD structured data: checks types, required properties and Google rich results eligibility, then gives PASS/WARN/FAIL per page with fixes. Not a title/meta generator.
$7
Ship Schema That Wins Rich Results: JSON-LD Audit
Example session with this skill installed
Run a schema audit on harborline-tools-sample.html and generate a markdown report for Harborline Tools.
- Read your context and instructions
- Compiled the ship schema that
- Generated the data export
I have audited the JSON-LD schema blocks in harborline-tools-sample.html. The analysis identified 3 schema blocks with 1 PASS, 1 WARN, and 1 FAIL status.
Schema Audit Report: Harborline Tools
| Type | Status | Missing Properties | Notes |
|---|---|---|---|
WebSite | PASS | None | Valid @context and url detected. |
Product | WARN | priceValidUntil, aggregateRating | Basic product data is present, but missing recommended conversion signals. |
FAQPage | FAIL | mainEntity | Block is empty or contains placeholder "example.com" content. Rich results will not trigger. |
BreadcrumbList | PASS | None | All itemListElement nodes contain valid position and item IDs. |
Property Gaps & Conflicts
- Product: The
offersobject is missingpriceValidUntil. Google may flag this as a warning in Search Console. - FAQPage: Found placeholder text "lorem ipsum" in the answer field of the second entity.
Organization: Multiple @type: Organization blocks detected; consider merging into a single @graph node to avoid crawler confusion.
Next steps
- Update the FAQPage
mainEntitywith real question and answer data from the production source. - Add the
priceValidUntilproperty to the Product JSON-LD, using a date formatted like2026-10-31. - Re-run the audit with
--csv out/audit-results.csvto export a full log for the engineering team.
harborline-tools-sample.html
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
This check is also inside the AI Search Visibility Suite ($22) with 5 others and a 0-100 score.
Schema markup audit for JSON-LD structured data: checks types, required properties and Google rich results eligibility, then gives PASS/WARN/FAIL per page with fixes. Not a title/meta generator.
Differentiation (read this first)
Unlike generic meta/title generators (and Cygnix seo-meta-tags-generator, which drafts titles, descriptions, and OG tags), this pack audits live JSON-LD for @type coverage, required properties, placeholders, and Google rich-result eligibility — then tells you what property to add. It never invents schema to force a PASS.
Sample output (offline dry-run on the included Harborline Tools HTML — 2026-10-16)
blocks=8 | PASS=1 | WARN=3 | FAIL=5 | types=[Organization, WebSite, Product, FAQPage, Article, BreadcrumbList]
| Type | Status | Missing / notes |
|---|---|---|
| Organization | WARN | Consider adding contactPoint |
| WebSite | WARN | Consider adding publisher |
| Product | FAIL | Missing offers/price; brand = "Your Company" placeholder |
| FAQPage | FAIL | Second Question missing acceptedAnswer |
| Article | FAIL | Missing required image |
| BreadcrumbList | FAIL + PASS | One empty block, one complete trail |
| (invalid HowTo) | FAIL | Illegal trailing comma in JSON-LD |
| Microdata | WARN | itemscope present — prefer JSON-LD |
The problem Agencies ship pages with broken or placeholder JSON-LD. Google Rich Results Test fails, FAQ/Product rich results never appear, and title/meta packs do not catch schema gaps.
What it does
- Extracts every
<script type="application/ld+json">block (also flags Microdata/RDFa) - Checks required/recommended properties for Article, Product, FAQPage, HowTo, Organization, LocalBusiness, BreadcrumbList, WebSite, SoftwareApplication, and more
- Flags invalid JSON, missing
@context/@type, empty required fields, placeholders (lorem,example.com,your-company), Product without offers/price, FAQ without Q&A, BreadcrumbList withoutitemListElement, duplicate conflicting types - Writes CSV + Markdown; stdout one-liner for agents/CI
- Offline-first, stdlib Python only — no paid APIs
Why this beats prompting it yourself Same type rules every run. Offline works unplugged. Never invents offers or FAQ answers. CSV your agent can diff in CI.
Use cases
- Pre-publish Product / FAQ / HowTo / Article schema gate
- CMS theme audits after a placeholder schema ship
- Agency deliverable: "JSON-LD rich-results audit" in one pass
Example session
"Audit this HTML for Google rich-result eligibility." →
blocks=8 | PASS=1 | WARN=3 | FAIL=5 | types=[Organization, WebSite, Product, FAQPage, Article, BreadcrumbList]→ Fix checklist: add Product.offers + priceCurrency; complete FAQ acceptedAnswer; add Article.image; remove trailing comma in HowTo block
Trust Works in Claude Code, Cursor and Codex · tested on the included Harborline sample · Oct 2026 · v1.0.0 · stdlib Python only · questions answered within 24h · Cygnix Labs
Pairs with: SEO Meta Tags Generator · Featured Snippet Optimizer · Content Refresh Audit · llms.txt + AI Crawler Access.
How to use
- 1
Connect your agent to the Agensi MCP
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- 2
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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Requires
Python