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Competitor Supplied Prices — Record Quality Audit, String Normalisation & IQR Outlier Flags
x402
Competitor Supplied Prices — Record Quality Audit, String Normalisation & 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 2026-10-19).
Audits up to 200 rows of competitor price data for record quality issues, normalises strings, and flags statistical outliers using IQR analysis across list_price and package_quantity fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Competitor supplied prices — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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 covering: (1) a record quality audit identifying missing, malformed, or inconsistent fields; (2) a string normalisation report highlighting and correcting formatting issues in text fields; and (3) IQR outlier flags identifying statistical anomalies in the selected numeric field (list_price or package_quantity) across all submitted rows.
Example use cases
Retail competitor pricing data clean-up
“I've scraped 120 rows of competitor price data with columns for row_id, company_group, date, list_price, and package_quantity — can you audit them for record quality problems, normalise any messy strings, and flag which list_price values are IQR outliers?”
Detecting anomalous package quantities
“We have 80 competitor pricing records and I'm worried some package_quantity values look wrong — can you run the IQR outlier check and record quality audit on those rows so I can see which ones are suspicious?”
Pre-analysis data validation for pricing team
“Before our pricing team analyses this competitor dataset of 200 rows, can you do a full quality audit, fix up any string normalisation issues, and produce the IQR outlier flags for list_price so we know which records to trust?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-07-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 competitor price records that need simultaneous data quality auditing, string normalisation, and IQR-based outlier detection — all in a single call for $0.01 USDC. Ideal for pre-processing competitor pricing datasets before downstream analysis, when you need three distinct diagnostic reports without making external lookups or predictions. Prefer this over manual inspection or single-purpose validators when you need all three reports in one operation on structured competitor pricing data.
Side effects
- Rows array empty or missing — validation error returned
- More than 200 rows submitted — request rejected with limit error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Field selector not one of 'list_price' or 'package_quantity' — enum validation error
- Missing required fields (row_id, company_group, date) on any row — partial or full rejection
- Numeric values outside allowed range — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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