Server errors appear intermittently after a theme or app change, and the merchant cannot say which URLs or how often. In this case, the immediate task is to investigate reported server errors affecting store pages. The workflow should produce an error inventory with repair and verification steps separating reproducible failures from intermittent ones.
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 reported server errors affecting store pages.
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: [each failing URL with timestamps in the store timezone, the triggering request and whether the visitor was signed in or checking out, plus the theme deploys, app installs and setting changes logged before the errors began]. Replay each reported request on demand to separate failures that reproduce every time from those that surface only under one condition, aligning error timestamps with the deploy record to name the most recent change that could explain them and marking anything that never reproduces as unconfirmed rather than fixed.
Return an error inventory with repair and verification steps, marking each item as reproduced or unconfirmed 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.
Record each failing URL with its timestamp in the store's timezone, the request that triggered it and whether the visitor was signed in or checking out, plus the theme deploys, app installs and setting changes logged shortly before the errors began.
Replay each reported request on demand to separate failures that reproduce every time from those that surface only under one condition, aligning error timestamps with the deploy record to name the most recent change that could explain them and marking anything that never reproduces as unconfirmed rather than fixed.
Deliver an error inventory with repair and verification steps giving each item its trigger, the steps that reproduce it, its reproduced or unconfirmed status, the suspected change and the rollback or patch proposed, then require the failed request to run clean repeatedly across the merchant's observation window before the item can be closed.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Because timestamps line failures up with deploys, traffic peaks and app events, and a mismatched timezone shifts all of it; without both you cannot say whether an error began before or after the change everyone suspects.
Leave it on the inventory as unconfirmed with everything known about its conditions instead of closing it, because intermittent failures tend to cluster around one trigger such as an empty cart, a specific checkout step or signed-in visitors, and losing those details loses the only lead available.
No, custom theme code, a recently installed app or a script added to the checkout is the likelier cause right after a change, so read the deploy record alongside the errors; platform-wide incidents appear regardless of recent changes and tend to hit every page rather than one template.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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