Step 1
Fix the ordered check list for this pass with, for each check, the evidence it must produce, the system or export it reads, and the person who will own the result.
The team runs different checks each time someone looks at store health, so results cannot be compared between cycles. In this case, the immediate task is to produce a repeatable store health report. The workflow should turn the chosen checks and accessible systems into a fixed sequence that reports evidence and owners the same way every cycle.
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 produce a repeatable store health report.
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 ordered check list, the evidence each check must produce, the systems and exports accessible for this pass, and the owner assigned to each check]. Run the checks in that fixed order, capture the evidence each one produces in the same fields every cycle, and carry forward any area without accessible evidence as unchecked, never as healthy.
Return a store health report with evidence, owners and next checks 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.
Fix the ordered check list for this pass with, for each check, the evidence it must produce, the system or export it reads, and the person who will own the result.
Run the checks in that fixed order, capture the evidence each one produces in the same fields every cycle, and carry forward any area without accessible evidence as unchecked, never as healthy.
Deliver the report with one row per check carrying its evidence, owner and the next check that would confirm a fix, then compare the row count against the check list and confirm the previous cycle's open items appear again with their new status.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The same checks in the same order producing the same evidence fields, so a changed value means the store changed rather than the method. When a check is added later, mark the cycles before it so the comparison stays honest.
As unchecked, with the reason the evidence was unavailable. Leaving the check out shortens the report but quietly narrows what the audit actually covered.
Only where the merchant asks for one, and always beside the evidence behind it. A grade on its own does not say which check failed or what the reader should do next.
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.