Step 1
Collect the measurements on record with the unit each was stated in, the unit the field must hold per target market, the conversion factors the merchant accepts for each unit pair, and the rounding precision required per field.
The catalog mixes two unit systems in one field while the store sells into markets that read them differently. In this case, the immediate task is to convert product measurements while preserving meaning. The workflow should produce an enriched product data draft that keeps each converted value beside its original and flags missing conversion factors.
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 convert product measurements while preserving meaning.
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 recorded measurements with their source units, the required target unit per market, the accepted conversion factors per unit pair, and the rounding precision required per field]. Convert every value using its accepted factor, keep the original figure and unit beside the converted one in the record, round only to the precision stated for that field, and flag values whose unit pair has no accepted factor, whose conversion depends on the product's own form or density, or whose source unit was never stated.
Return an enriched product data draft with unresolved fields 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 measurements on record with the unit each was stated in, the unit the field must hold per target market, the conversion factors the merchant accepts for each unit pair, and the rounding precision required per field.
Convert every value using its accepted factor, keep the original figure and unit beside the converted one in the record, round only to the precision stated for that field, and flag values whose unit pair has no accepted factor, whose conversion depends on the product's own form or density, or whose source unit was never stated.
Deliver an enriched draft listing original value, original unit, converted value, factor used and rounding applied, together with an unresolved list for flagged rows, then reverse a sample back into the source unit and confirm each result lands within the merchant's allowed rounding step.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No, it stays beside the converted one with its unit named. Keeping it makes any later dispute checkable and lets the storefront display whichever form each market expects; replacing it removes the only evidence of what was actually measured in the first place.
Exactly the precision the merchant states for the field, and no more. Extra decimals imply accuracy the source does not have, while rounding too hard distorts small pack sizes, so any value moved beyond the allowed step is flagged rather than quietly published.
When the two units measure different things, such as volume to weight, where the result depends on that product's form or density. Those conversions need a factor specific to the item; with only a generic factor, the row is left unresolved rather than given a number that looks measured but is not.
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.