After a recent app change the store no longer behaves as expected, and the logs do not say which app is responsible. In this case, the immediate task is to investigate conflicts between store apps. The workflow should produce an integration diagnosis and recovery plan listing each tested change and its observed effect.
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 investigate conflicts between store apps.
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: [installed app list with permissions and written records, recent changes with timestamps, error logs and console output, the expected data flow between apps, and the records showing the symptom]. Isolate the failing boundary by testing one app at a time against the stated expected behaviour, disabling or reconfiguring a single app, reproducing the symptom and restoring the original state before the next test, and recording the observed effect after each change rather than after a batch of them.
Return an integration diagnosis and recovery plan listing each tested change and its observed effect 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 installed app list with each app's permissions and the records it writes, the changes made in the recent window with their timestamps, error logs and console output, the expected data flow between apps, and the records that show the symptom.
Isolate the failing boundary by testing one app at a time against the stated expected behaviour, disabling or reconfiguring a single app, reproducing the symptom and restoring the original state before the next test, and recording the observed effect after each change rather than after a batch of them.
Deliver the integration diagnosis and recovery plan listing each tested change and its observed effect, then verify that the symptom reproduces with the identified app enabled and disappears once it is reconfigured or disabled, and that the plan states the rollback position for every change it proposes.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Yes, when both read or write the same record or field with different assumptions about timing or update order. Each app passes its own tests while the shared field ends up holding whichever value was written last.
Changing several components together hides which one mattered and can break unrelated workflows at the same moment. Restoring the original state between tests keeps each observed effect attributable to a single reversible action, which makes the diagnosis repeatable later.
A symptom that reproduces whenever the suspected app is enabled and disappears when it is reconfigured or disabled, observed on the same records. A fix that merely reduces frequency is logged as unconfirmed and kept under observation rather than reported as resolved.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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