The same ad image must run across placements with different aspect ratios, and the records list which placements matter and which facts must stay visible. In this case, the immediate task is to adapt ad image layouts to different placements. The workflow should produce recomposed layouts with copy or image briefs, each checked against the target placement.
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 adapt ad image layouts to different placements.
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 source ad files, the placement list with each aspect ratio and safe area, the text and offer elements that must stay visible, the product facts and approved claims, and the accepted crop behaviour for the subject]. Recompose each layout for its target shape by repositioning the subject and resetting type size and margins rather than scaling the whole frame, keeping the offer wording identical across every variant, and marking any placement where a required element cannot fit as needing a new composition rather than forcing it in.
Return the adapted layouts with copy or image briefs, each verified against the supplied placements 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.
Collect the source ad files, the placement list with each aspect ratio and safe area, the text and offer elements that must remain visible, the product facts and approved claims on record, and the crop behaviour the merchant accepts for the subject.
Recompose each layout for its target shape by repositioning the subject and resetting type size and margins rather than scaling the whole frame, keeping the offer wording identical across every variant, and marking any placement where a required element cannot fit as needing a new composition rather than forcing it in.
Deliver one layout per placement with a copy or image brief noting what moved and what was dropped, then check each against the target aspect ratio and safe area and confirm that the offer text on every variant still matches the destination page.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Uniform scaling shrinks type and pushes the subject toward the edge, so the offer can fall below legible size while the layout still looks intact; recomposing per ratio keeps the hierarchy, which is what the placement actually crops.
The placement is marked as needing its own composition, usually with a shorter headline or a tighter crop of the subject, because squeezing a required element in produces a variant that fails review rather than one that merely looks plain.
Yes, in the same currency and wording as the destination page, since the variant a shopper sees may be the only one they act on and a missing price sends traffic that arrives expecting a different number.
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.