Step 1
Collect each SKU's stock on hand and cost basis, the date and type of its last recorded movement, the analysis period the merchant sets, and the open order lines that already reserve or promise those units.
Some SKUs have not moved recently, but open orders may already cover them, so a movement gap alone proves little. In this case, the immediate task is to identify stock with little or no recent movement. The workflow should produce an inventory health analysis and prioritized actions, checking each flagged SKU against open orders before calling it dead.
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 identify stock with little or no recent movement.
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: [stock on hand and cost basis per SKU, the date and type of each SKU's last recorded movement, the merchant's analysis period, and the open order lines reserving those units]. Flag a SKU as unmoved only when neither a sale, a receipt, a transfer nor an adjustment appears for it inside the period, then check the flagged units against open orders and drop from the dead list every SKU whose remaining units are already committed, reporting only the uncommitted remainder.
Return an inventory health analysis and prioritized actions 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 each SKU's stock on hand and cost basis, the date and type of its last recorded movement, the analysis period the merchant sets, and the open order lines that already reserve or promise those units.
Flag a SKU as unmoved only when neither a sale, a receipt, a transfer nor an adjustment appears for it inside the period, then check the flagged units against open orders and drop from the dead list every SKU whose remaining units are already committed, reporting only the uncommitted remainder.
Deliver an inventory health analysis with one row per flagged SKU showing uncommitted units, cost tied up and time since last movement, rank the prioritized actions by value at stake, and reconcile flagged quantities with the inventory export and the open order list before publishing.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No; a SKU is only called dead after its flagged units are checked against open orders, because stock already promised to a customer is working stock even when no movement posted inside the window. Only the uncommitted remainder is reported as dead, and the committed portion is listed separately with the order it belongs to.
Any recorded sale, receipt, transfer or adjustment inside the period, not sales alone, since a restock or an internal transfer shows the SKU is still being handled. A manual adjustment counts too but is shown with its reason, so a correction is never mistaken for real demand.
Kept in the report rather than dropped, because the units still hold cost and still need a disposition decision. They are labelled discontinued where that status is recorded and ranked by value tied up, so they are not quietly written off through omission.
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.