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Promotion Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Promotion Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Runs three local analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 promotion observation records containing discount_amount and units_sold fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Promotion observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied promotion observations records (discount_amount, units_sold). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports returned together: (1) a record quality audit flagging completeness and validity issues across the supplied rows, (2) a string normalisation report identifying and standardising inconsistent string values in store_group and other text fields, and (3) an adjacent row changes report highlighting sequential differences in the selected numeric field (discount_amount or units_sold). All computed locally with no external lookups.
Example use cases
Retail promotion data quality check
“I have about 80 rows of promotion data with store groups, dates, discount amounts, and units sold. Can you audit the record quality, normalise the store group strings, and flag any adjacent row changes in the discount_amount field all at once?”
Detecting sequential discount anomalies
“I want to find where my discount_amount jumps unexpectedly between consecutive rows in my promotions dataset — can you run the adjacent row changes report alongside a quality audit on these 150 promotion observation records?”
Cleaning units sold promotion records
“My promotion observations have inconsistent string formatting in the store_group column and I suspect some units_sold values have quality issues. Can you run string normalisation, record quality audit, and adjacent row changes reports on these records focusing on the units_sold field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-05-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data quality reports (record quality audit, string normalisation, and adjacent row changes) for promotion observation data in a single low-cost call. It is ideal for retail analysts who want to audit and clean promotion datasets of up to 200 rows without requiring external data lookups, predictions, or professional assessments. Prefer it over general-purpose data tools when working specifically with discount_amount and units_sold promotion records and needing all three report types simultaneously.
Side effects
- More than 200 rows supplied — request exceeds the row limit and will be rejected
- Missing required fields (row_id, store_group, date, discount_amount, units_sold) in any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- field enum value not one of 'discount_amount' or 'units_sold' — rejected
- Empty rows array — minItems:1 constraint violated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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