Step 1
Take the supplied markup sample, the validation messages returned for it, the page each sample came from and the product types involved.
A validation tool flagged a batch of schema errors, but the raw messages do not say what to fix or why. In this case, the immediate task is to validate supplied structured data and explain errors. The workflow should classify each validation message as a missing required field, a formatting problem or a conflicting value, and attach a fix to every error.
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.
Act as a Shopify operations specialist.
Help me validate supplied structured data and explain errors.
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 supplied markup samples with their source pages, the validation messages returned for each sample, the product types involved and the templates or data fields behind them]. Classify every message as a required-field gap, a formatting problem or a value conflicting with the page, and attach to each the property name, the reason it failed and the concrete fix, keeping warnings separate from blocking errors.
Return a structured-data issue report and correction draft with each error explained 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.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Take the supplied markup sample, the validation messages returned for it, the page each sample came from and the product types involved.
Classify every message as a required-field gap, a formatting problem or a value conflicting with the page, and attach to each the property name, the reason it failed and the concrete fix, keeping warnings separate from blocking errors.
Deliver the issue report with each message explained and its fix, grouped by the shared template or data source behind it, then re-run validation on the corrected sample and confirm the message no longer appears.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
A missing required property or a value invalid for the declared type, because either stops the block being read. Warnings cover optional properties and can be scheduled separately.
They usually come from one shared template or one empty data field, so correcting the source clears every affected page at once. Editing page by page leaves the next import to reintroduce them.
No. Rank warnings by whether the property serves the merchant's stated goal and whether shoppers see the value, then schedule the rest rather than blocking the submission on them.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.