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Replenishment Timing Data Quality Suite
x402
Replenishment Timing Data Quality Suite 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).
Audits, normalises strings, and fills missing values in replenishment timing records (lead_days, received_units) across up to 200 rows, returning 3 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Replenishment timing — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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: (1) a record quality audit flagging issues across all rows, (2) a string normalisation report for store_group and text fields, and (3) an explicit missing-value fill report for the specified numeric field (lead_days or received_units) using the chosen method (zero, mean, or median). All reports are generated locally with no external lookups.
Example use cases
Pre-load inventory data validation
“Before I import these replenishment timing records into our warehouse system, can you run a full quality audit on them, normalise the store_group strings, and fill any missing lead_days using median? I've got 120 rows with row_id, store_group, date, lead_days, and received_units.”
Supply chain lead time data cleaning
“I have 80 rows of supplier lead time data with some nulls in the lead_days column and inconsistent capitalisation in store_group — can you audit the record quality, fix the string formatting, and impute missing lead_days using the mean?”
Received units gap filling for retail report
“My retail replenishment dataset has about 60 rows and several missing received_units values. Can you do a quality audit, normalise the store group names, and fill those gaps using zero imputation so I can prepare the report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-12-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality pass on replenishment timing records covering quality auditing, string normalisation, and missing-value imputation in a single call for up to 200 rows at low cost. It is best suited for offline batch data cleaning before ETL ingestion, not for real-time predictions or live inventory lookups.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, store_group, date, lead_days, received_units) in any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- Invalid enum value for method or field — schema rejection
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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