Each team keeps its own stock file, and the figures disagree because nobody has agreed which source wins. In this case, the immediate task is to consolidate stock information for operational decisions. The workflow should produce an inventory visibility report and refresh requirements traced back to each source's own records.
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 consolidate stock information for operational decisions.
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: [every stock data source with its SKU identifiers and location scope, the SKU mapping linking the sources, the agreed precedence order for conflicts, and each source's update cadence and last successful refresh]. Merge the sources into one position per SKU and location by applying the agreed precedence, keep the losing figure and its source visible beside the consolidated one rather than discarding it, and flag any SKU whose mapping is missing or whose source has stopped refreshing instead of filling the gap with another source's number.
Return an inventory visibility report and refresh requirements 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 every stock data source with its SKU identifiers and location scope, the SKU mapping that links the sources together, the precedence order the merchant has agreed for conflicting figures, and each source's stated update cadence and last successful refresh.
Merge the sources into one position per SKU and location by applying the agreed precedence, keep the losing figure and its source visible beside the consolidated one rather than discarding it, and flag any SKU whose mapping is missing or whose source has stopped refreshing instead of filling the gap with another source's number.
Deliver an inventory visibility report carrying the consolidated position, the conflicts resolved and the exceptions, add refresh requirements naming each source's true interval and owner, and reconcile the row count with the SKU master and confirm every source's last refresh timestamp before publishing.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The one the merchant placed highest in the agreed precedence, applied consistently to every SKU and location. The losing figure is not deleted but shown beside the consolidated value with its source, because a source that is quietly always overridden is usually a broken feed rather than a simply wrong one.
No faster than the slowest upstream source actually delivers, and the true interval is shown rather than the intended one. If a source has missed its cadence, the view carries the older timestamp and lists the affected SKUs, so nobody reads a merged figure as current.
Only SKUs whose mapping is complete across the sources. Unmapped and archived SKUs go to an exceptions list, retrievable for earlier reporting periods but kept out of operational decisions, since a merged figure built on a guessed mapping is worse than no figure at all.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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