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Warehouse Bins — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Warehouse Bins — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Runs three local analytical reports on warehouse bin records: data quality audit, string normalisation, and trailing mean series computation for occupied_volume or available_volume.
Price / call$0.01
Success—
StatusUnknown
How this service works
Warehouse bins — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied warehouse bins records (occupied_volume, available_volume). 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
Returns three independent analytical reports for the submitted warehouse bin rows: (1) a record quality audit flagging nulls, format violations, and anomalies; (2) a string normalisation report for fields like store_group; and (3) a trailing mean series for the chosen numeric field (occupied_volume or available_volume) over the specified window. All computed locally with no external lookups.
Example use cases
Spot data gaps in bin records
“I have 50 warehouse bin rows from last quarter — can you audit them for quality issues like nulls or bad dates, normalise the store group strings, and give me a trailing mean of occupied_volume with a window of 5?”
Standardise store labels before reporting
“Our store_group field has inconsistent capitalisation across 120 bin records. Run the quality audit and string normalisation reports on them so I can clean up before the monthly dashboard refresh — use occupied_volume as the field and window 4.”
Rolling average for available volume trend
“Calculate a trailing mean series for available_volume with a window of 7 across these 80 warehouse bin records, and include the quality audit so I know if any rows are problematic.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-13-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and trailing mean — on warehouse bin data (occupied_volume/available_volume) in a single low-cost call. It is purely offline/local computation with no external data enrichment or prediction, making it ideal for batch pre-processing or data validation pipelines before downstream reporting.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside 2–200 — schema rejection
- Field value not in enum (occupied_volume, available_volume) — validation error
- Numeric values outside ±1,000,000,000,000 — schema rejection
- Missing required fields (row_id, store_group, date) — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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