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Replenishment Timing String Normalisation + Date by Category Pivot
x402
Replenishment Timing String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Runs two local analytical reports on replenishment timing records: string normalisation and a date-by-category pivot, for up to 200 rows of lead_days or received_units data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Replenishment timing — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied replenishment timing records (lead_days, received_units). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two local analytical reports: (1) a string normalisation report cleaning and standardising the store_group field values, and (2) a date-by-category pivot table aggregating the selected numeric field (lead_days or received_units) broken down by date and store_group category — both derived from the supplied rows with no external data lookups.
Example use cases
Lead days pivot by store group
“I have 120 replenishment records with store groups like 'group-a' and 'group-b', dates from last quarter, and lead_days values — can you run string normalisation and pivot the lead_days field by date and category so I can see timing patterns across groups?”
Received units category analysis
“Run the two analytical reports on my replenishment timing data — I want string normalisation and a date-by-category pivot for received_units across 80 rows covering our retail store groups from October 2026.”
Retail replenishment data cleanup
“My replenishment dataset has inconsistent store group labels and I need a clean pivot of received_units by date and store category — can you normalise the strings and produce the date pivot for these 30 records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-12-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two quick, self-contained analytical reports — string normalisation plus a date-by-category pivot — on replenishment timing data without any live lookups or external predictions. Best suited for small-to-medium datasets (up to 200 rows) of retail replenishment records with store group, date, lead_days, and received_units fields, where the analysis runs entirely on supplied data.
Side effects
- Input exceeds 200 rows — request rejected
- Invalid date format (must be YYYY-MM-DD) — validation error
- Missing required fields (row_id, store_group, date, lead_days, received_units) — schema error
- field enum value not recognised — validation error
- Numeric values outside allowed range — schema rejection
- Empty rows array — minimum items not met
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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