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Replenishment Timing Data Auditor — Record Quality, String Normalisation & Base-100 Series
x402
Replenishment Timing Data Auditor — Record Quality, String Normalisation & Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on replenishment timing rows (lead_days, received_units): a record quality audit, string normalisation, and a base-100 indexed series.
Price / call$0.01
Success—
StatusUnknown
How this service works
Replenishment timing — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied replenishment timing records (lead_days, received_units). 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 missing, null, or out-of-range values in the supplied rows; (2) a string normalisation report on text fields like store_group; (3) a base-100 indexed series for the selected numeric field (lead_days or received_units) anchored to the specified base_index row. All computed locally with no external lookups.
Example use cases
Inventory data quality check before reporting
“I have 150 replenishment timing records with lead_days and received_units — can you audit them for quality issues, normalise the store_group strings, and give me a base-100 index of lead_days starting from the first row?”
Supply chain analyst baseline indexing
“Run a base-100 series on the received_units field across my replenishment rows, plus a record quality audit and string normalisation — I have about 80 rows from three store groups and want to spot anomalies.”
Retail ops data cleaning before pipeline load
“Before I load this batch of 200 replenishment timing rows into our pipeline, can you check record quality, normalise the store_group strings, and produce a base-100 lead_days series using row index 0 as the base?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-12-201?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have up to 200 replenishment timing records and need all three of a quality audit, string normalisation, and base-100 series in a single low-cost call. Ideal for retail or supply chain pipelines needing lightweight, local, offline data validation and indexing without any external data lookups or predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, store_group, date, lead_days, received_units) cause schema validation failure
- base_index out of range (>= number of rows) returns an error
- Null numeric values in the base_index row may cause base-100 calculation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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