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Price Basket Observations — String Normalisation + Adjacent Row Changes
x402
Price Basket Observations — String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Analyzes price basket observation records by running string normalization and adjacent row change detection on item_price or quantity_weight fields, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Price basket observations — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied price basket observations records (item_price, quantity_weight). 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 independent analytical reports: (1) a string normalization report cleaning and standardizing string fields in the basket records, and (2) an adjacent row changes report identifying value differences between consecutive rows for the selected field (item_price or quantity_weight). Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Retail price trend audit
“I have a week of price basket records for two store regions — can you normalize the string fields and flag any row-to-row changes in item_price across my 20 observations?”
Quantity weight anomaly detection
“Check my 50 basket observation rows for sequential changes in quantity_weight and normalize any inconsistent strings — the rows cover multiple regions and dates in 2025.”
Economics dataset QA before reporting
“Before I submit this price basket dataset, run string normalization and adjacent row change detection on the item_price field across all 150 rows so I can catch any oddities.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-07-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously clean string data and detect sequential row-level changes in price basket observations without any external API calls, live lookups, or predictions. It is ideal for economics data quality workflows where you want two distinct analytical perspectives (normalization + delta detection) in a single cheap call ($0.01 USDC) on datasets up to 200 rows.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required field (row_id, region, date, item_price, or quantity_weight) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Field selector not one of 'item_price' or 'quantity_weight' — enum validation error
- Empty rows array — minItems constraint violation
- Numeric values outside allowed range (±1 trillion) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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