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Competitor Supplied Prices — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Competitor Supplied Prices — 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 2026-10-19).
Runs three analytical reports on competitor price data: record quality audit, string normalisation, and trailing mean series for list_price or package_quantity fields, across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Competitor supplied prices — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied competitor supplied prices records (list_price, package_quantity). 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 returned together: (1) a record quality audit flagging missing, null, or out-of-range values in list_price and package_quantity; (2) a string normalisation report identifying and cleaning inconsistencies in company_group labels; (3) a trailing mean series for the selected numeric field (list_price or package_quantity) over the specified window size, computed locally without any external lookups.
Example use cases
Pre-analysis data quality check
“I've got up to 150 rows of competitor pricing data with list_price and package_quantity — can you run the record quality audit and string normalisation reports on them before I feed them into my pricing model?”
Trailing mean for competitor list prices
“Run a trailing mean series with a window of 5 on the list_price field for these competitor records — I want to see the rolling average trend across my company groups.”
Spot string inconsistencies in company groups
“Can you normalise the company_group strings in my competitor pricing dataset and flag any formatting inconsistencies or bad records? I have about 80 rows to check.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-07-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 competitor pricing records and need simultaneous data quality auditing, string normalisation of categorical labels, and rolling statistical analysis — all in one paid call at $0.01 USDC. It is ideal for pre-processing competitor pricing data before modelling or reporting, especially when you want three complementary analytical views without multiple round-trips. Prefer this over single-purpose audit or normalisation tools when all three report types are needed together.
Side effects
- Input array is empty or exceeds 200 rows — validation error returned
- Invalid date format (not YYYY-MM-DD) in any row — schema rejection
- Selected field value not in enum ['list_price', 'package_quantity'] — schema error
- Window size outside 2–200 range — validation failure
- Malformed numeric values beyond allowed range — rejection
- Missing required fields (row_id, company_group, date) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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