01
Step 1
Pull, for each product and the variant URLs that resolve to it, the rendered page, its structured-data block and the price, currency and availability the page displays at that moment.
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Spark playbook scenarios · Spark: AI Store Assistant
Compare product schema with visible product information
Search results and shopping surfaces read structured data that may silently disagree with the price and availability shoppers see. In this case, the immediate task is to compare product schema with visible product information. The workflow should match each markup property against the visible facts on the rendered pages and recheck every corrected field.
Topic
Structured Data and Schema
Target keyword
shopify product schema audit
Copyable AI prompt
A prompt you can drop directly into Spark
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me compare product schema with visible product information.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the rendered product and variant pages, the structured-data block on each, the price with currency and availability shown on the page, and the merchant's product identifier used to pair them]. Match every markup property against the visible fact it claims to describe and mark the ones that conflict, duplicate another block on the same page or stay empty, using the rendered value as the reference rather than the admin value.
Return a structured-data issue report and correction draft for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Match every markup property against the visible fact it claims to describe and mark the ones that conflict, duplicate another block on the same page or stay empty, using the rendered value as the reference rather than the admin value.
03
Step 3
Deliver the issue report and correction draft per URL, then recheck each corrected field against a fresh render and confirm it now equals the visible fact, listing any field where the two still disagree.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Which properties should be compared first?
Price, availability and product identity, because those are what shopping surfaces read and what shoppers compare against the page. Description mismatches matter less and rarely mislead a buyer.
How is an empty property recorded?
As missing rather than wrong, with a note on whether the value exists in the merchant's own records. Adding a value with no source behind it creates a mismatch that is harder to find later.
Does one template correction fix every product page?
Only where the underlying product data is complete. Pages with a missing field or variant-specific pricing still render something different and have to be rechecked individually.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Check page schema against visible product facts
Validate supplied structured data and explain errors
Diagnose incorrect or inconsistent displayed product prices
Extract supported product facts from supplied product URLs
Investigate missing or confusing variant information on a page
Detect repeated descriptions that obscure product differences
Next step
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