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Spark playbook scenarios · Spark: AI Store Assistant

Identify missing or invalid variant prices

Variants carry no price, a zero price or a figure outside the range the merchant set, which leaves those options unbuyable. In this case, the immediate task is to identify missing or invalid variant prices. The workflow should produce a prioritized product issue report that separates true gaps from data errors and names the price field behind each one.

Topic
Product Data Quality and Audits
Target keyword
shopify missing variant prices
Copyable AI prompt

A prompt you can drop directly into Spark

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.

ai-prompt.txt
Act as a Shopify operations specialist.
Help me identify missing or invalid variant prices.
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 variant list with price, compare-at price and currency fields, the active currency setting on the store, and the stated price bounds and rounding step]. Test every variant for an empty price, a zero or negative price, a value outside the stated bounds or a value written in a format the price field cannot accept, then split the findings into true gaps where no figure exists and data errors where a wrong figure does.
Return a prioritized product issue report with evidence and suggested fixes 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.
Use in Spark
How to solve

A recommended resolution workflow

Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.

01

Step 1

Pull the variant list with its price, compare-at price and currency fields, the active currency setting on the store, and the price bounds and rounding step the merchant has stated for this catalog.

02

Step 2

Test every variant for an empty price, a zero or negative price, a value outside the stated bounds or a value written in a format the price field cannot accept, then split the findings into true gaps where no figure exists and data errors where a wrong figure does.

03

Step 3

Deliver a prioritized product issue report naming the field behind each finding, its current raw value and the suggested fix, and reconcile the flagged count against the variant count in the supplied list so no variant goes unchecked.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

Is a zero price a gap or an error?

Treat it as an error, because a figure was entered but cannot be right for a sellable item. A blank field is a gap and needs a value requested from the merchant instead, never a copied parent price.

How are prices entered in the wrong currency handled?

Flag them and keep the raw figure rather than converting silently, since the rate and its date would be an assumption. Ask the merchant which currency the figure was entered in before any correction is applied.

Which price findings should be fixed first?

Purchasable variants whose price field is empty, because checkout cannot complete for them. Unpublished or out-of-stock variants can wait for the next catalog pass, but they should still be listed.

Related scenarios

Continue with adjacent or same-topic scenarios

Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.

Next step

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