01
Step 1
Export every product image URL with its product and variant associations, gather the merchant's screenshots of where failures appear, and note which URLs point to external hosts rather than the store's own file storage.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify failed product image URLs and affected listings
Some listings render blank because media links no longer resolve, and the merchant holds screenshots of where it happens. In this case, the immediate task is to identify failed product image URLs and affected listings. The workflow should produce an issue inventory and replacement plan drawn from those exports, ranked by how visible each listing is.
Topic
Product Image Quality and Errors
Target keyword
shopify broken product 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 identify failed product image URLs and affected listings.
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 exported image URLs with product and variant associations, merchant failure screenshots, and external host flags]. Request each URL in turn and classify every failure as a missing file, a renamed asset, an expired external host or a wrong product association, then rank the affected listings by visibility with the primary image of currently purchasable products first.
Return an issue inventory and a replacement plan rechecked URL by URL against the supplied records 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
Request each URL in turn and classify every failure as a missing file, a renamed asset, an expired external host or a wrong product association, then rank the affected listings by visibility with the primary image of currently purchasable products first.
03
Step 3
Deliver the issue inventory with a replacement plan that replaces assets the product still needs and removes only those the merchant confirms retired, then re-test each fixed URL and match it against the original export before closing the case.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Why classify broken images by cause?
Because the same visible gap fixes differently: a missing file needs a re-upload, a renamed asset a link update, an expired external host a re-host, and a wrong association a catalog correction; acting without the class means re-breaking the same images at the next import.
Which failures are fixed first?
Primary images on currently purchasable listings, then secondary images on the same listings, then hidden or draft records, because a blank hero on a live product blocks purchase decisions while a broken gallery image only weakens them.
How is a fix confirmed?
The corrected URL is requested again and compared with the original export's record for the same file slot, and the listing is checked in the channel the merchant's screenshot came from, so the fix is verified exactly where the failure was observed.
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Next step
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