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Step 1
Start with catalog export, required-field rules, verified source data and relevant variant relationships.
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Spark playbook scenarios · Spark: AI Store Assistant
Find variants that lack the correct product image
A catalog has incomplete or contradictory information that is hard to review product by product. In this case, the immediate task is to find variants that lack the correct product image. The workflow should produce a prioritized product issue report with evidence and suggested fixes, using the merchant's actual records and constraints.
Topic
Product Data Quality and Audits
Target keyword
shopify missing variant images
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 find variants that lack the correct product image.
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: [provide catalog export, required-field rules, verified source data and relevant variant relationships].
Check completeness and consistency, identify affected IDs and distinguish missing evidence from confirmed errors; propose fixes by customer impact.
Return a prioritized product issue report with evidence and suggested fixes 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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
A catalog has incomplete or contradictory information that is hard to review product by product. In this case, the immediate task is to find variants that lack the correct product image. The workflow should produce a prioritized product issue report with evidence and suggested fixes, using the merchant's actual records and constraints.
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
Check completeness and consistency, identify affected IDs and distinguish missing evidence from confirmed errors; propose fixes by customer impact.
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Step 3
Deliver a prioritized product issue report with evidence and suggested fixes; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Does a missing field always make a listing unusable?
No. Impact depends on the field and selling context. Missing price or essential variant information may matter more than an optional attribute.
Why distinguish missing data from incorrect data?
A blank field needs enrichment, while a wrong value needs correction. Treating both the same can overwrite valid information.
What information is needed to investigate this use case?
Start with catalog export, required-field rules, verified source data and relevant variant relationships. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
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.
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Run a repeatable product data audit
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Design recurring audits for product data quality
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Assess listing completeness using explicit review criteria
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Next step
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