Step 1
Collect the supplier's column headers, sample rows covering every column, the destination product fields with their permitted values, and the fill conventions already used in the store.
Before any upload, the supplier's column names have to be reconciled with the store's fields, and several are ambiguous. In this case, the immediate task is to map supplier CSV fields to the intended product fields. The workflow should produce a mapping table for merchant approval, matched against the merchant's own conventions and sample rows.
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 map supplier CSV fields to the intended product fields.
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 supplier's column headers, sample rows covering every column, the destination product fields with their permitted values, and the store's existing fill conventions]. Match each source column to exactly one destination field by comparing its actual contents rather than its label, writing beside each pair the validation rule and one example value lifted from the supplier file, and marking ambiguous any column whose contents fit two destination fields, whose values fall outside the destination's permitted set, or whose units or formats disagree with the store convention.
Return a mapping table for merchant approval before any upload 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 supplier's column headers, sample rows covering every column, the destination product fields with their permitted values, and the fill conventions already used in the store.
Match each source column to exactly one destination field by comparing its actual contents rather than its label, writing beside each pair the validation rule and one example value lifted from the supplier file, and marking ambiguous any column whose contents fit two destination fields, whose values fall outside the destination's permitted set, or whose units or formats disagree with the store convention.
Deliver the mapping table for merchant approval before any upload, listing each source column with its destination field, rule, sample value and any transformation required such as unit restatement or value remapping, and prove it by running sample rows covering every mapped column and reviewing the draft products they produce.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
By reading the column's actual contents. A header called variant detail holding colour words belongs in the option field whatever it is named, while one calling itself colour may carry codes needing remapping. Labels are noted, contents decide, and every ambiguous column goes to the merchant rather than being fitted by guesswork.
A sample run across a few rows chosen to cover every mapped column, reviewed as draft products. A file that uploads without complaint proves only that nothing crashed, whereas drafts showing correct options, units and values are real evidence, which is why the samples travel with the approval request.
Leave them out of the mapping but keep them in the source file, recording why each was excluded. Deleting columns destroys context a later debugging session needs, and an unused column today often turns out to be the only place some required detail was ever recorded.
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.