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Warehouse Bins String Normalisation and IQR Outlier Flags
x402
Warehouse Bins String Normalisation and IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Normalises store_group strings and flags IQR-based outliers in occupied_volume and available_volume fields for up to 200 warehouse bin records, producing two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Warehouse bins — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied warehouse bins records (occupied_volume, available_volume). 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 analytical reports per call: one covering string normalisation results for the store_group field (cleaned/standardised values), and one covering IQR-based outlier flags for the selected numeric field (occupied_volume or available_volume), indicating which rows fall outside the interquartile range bounds.
Example use cases
Flagging anomalous bin fill levels
“Here are 150 warehouse bin records with occupied_volume and available_volume for October — can you normalise the store_group labels and flag any IQR outliers in occupied_volume so I can spot bins with abnormal fill levels?”
Data quality check before reporting
“Before I send this warehouse dataset to finance, can you run string normalisation on the store_group field and check available_volume for statistical outliers using IQR across these 80 bin records?”
Retail DC capacity anomaly review
“I have bin-level records across two distribution centre groups — dates range from 2026-10-01 to 2026-10-31, up to 200 rows — please normalise the store_group strings and give me IQR outlier flags on the occupied_volume figures.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-13-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have warehouse bin records (up to 200 rows) that need both string normalisation on categorical identifiers and IQR-based outlier detection on volume metrics in a single low-cost call. It is especially suited for pre-reporting data quality pipelines, inventory anomaly screening, or retail logistics dashboards where both cleaning and statistical flagging are needed together without external lookups or predictions.
Side effects
- Payload exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, occupied_volume, available_volume) — schema validation failure
- Date field not in YYYY-MM-DD format — pattern validation error
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Insufficient variance in numeric data may produce no outlier flags
- Empty rows array — minItems constraint violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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