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Returns Handling — String Normalisation + Trailing Mean Series
x402
Returns Handling — 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 string normalisation and trailing mean series analytics on retail returns data (returned_units, processing_minutes) for up to 200 rows, producing two local reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Returns handling — String normalisation + Trailing mean series: String normalisation + Trailing mean series for supplied returns handling records (returned_units, processing_minutes). 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: (1) a string normalisation report cleaning and standardising string fields in the supplied returns records, and (2) a trailing mean series for the selected numeric field (returned_units or processing_minutes) computed over the specified sliding window — all derived locally from the supplied data, with no external lookup or prediction.
Example use cases
Retail returns trend smoothing
“I have 120 rows of daily returns data across store groups A, B, and C with returned_units and processing_minutes — can you compute a trailing mean series with a window of 5 on returned_units, along with string normalisation on the records?”
Processing time efficiency analysis
“Run a trailing mean series with window size 10 on processing_minutes for my 80-row returns dataset, and also normalise the string fields — I want to see if our processing times are trending up or down across store groups.”
Small-batch returns audit
“I've got 30 returns handling records with row_id, date, store_group, returned_units, and processing_minutes. Can you run the string normalisation report and a trailing mean (window 2) on returned_units to get a quick overview?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-04-028?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local analytical computation on retail returns data — specifically string normalisation combined with a trailing mean series — without requiring any live data lookup, external prediction, or professional assessment. It is cost-effective at $0.01 USDC for two reports on up to 200 rows. Prefer it over general-purpose analytics tools when you already have structured returns records and need a fast, deterministic dual-report output.
Side effects
- Fewer than 1 or more than 200 rows submitted — returns validation error
- Window size larger than number of rows — may produce empty or insufficient series
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, date, returned_units, processing_minutes) — rejected
- Non-numeric values in returned_units or processing_minutes where numbers expected — null handling or error
- Field enum value not one of 'returned_units' or 'processing_minutes' — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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