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Competitor Supplied Prices — Record Quality Audit + String Normalisation + Numeric Distribution
x402
Competitor Supplied Prices — Record Quality Audit + String Normalisation + Numeric Distribution 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 local analytical reports (record quality audit, string normalisation, numeric distribution) on up to 200 rows of competitor price data containing list_price and package_quantity fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Competitor supplied prices — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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
Returns three independent analytical reports: (1) a record quality audit flagging completeness, consistency, and validity issues per row; (2) a string normalisation report standardising text fields like company_group; and (3) a numeric distribution summary (e.g. min, max, mean, percentiles) for the selected numeric field (list_price or package_quantity). All analysis is performed locally with no external lookups.
Example use cases
Pricing data quality check before analysis
“I've collected competitor prices from several suppliers and want to make sure the data is clean before I use it — can you run a record quality audit, string normalisation, and numeric distribution report on these 80 rows of list_price and package_quantity data?”
Detecting outliers in competitor package quantities
“Can you run a numeric distribution analysis on the package_quantity field across these 120 competitor pricing rows grouped by company_group so I can spot outliers or skewed distributions before benchmarking?”
Pre-processing competitor data for reporting
“Before I build my pricing comparison report, can you audit these 150 competitor price records for quality issues, normalise any inconsistent string values, and summarise the numeric distribution of list_price by date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-07-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw competitor pricing data (up to 200 rows) that needs simultaneous quality auditing, string standardisation, and numeric distribution profiling in a single low-cost call. It is ideal as a pre-processing step before deeper competitive analysis, when you want three bundled analytical views without building separate pipelines. Prefer it over general-purpose data tools when your data specifically includes list_price and package_quantity fields from competitor or supplier sources.
Side effects
- Payload exceeds 200 rows — request rejected
- Missing required fields (row_id, company_group, date, list_price, or package_quantity) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array — minimum 1 item required
- Invalid field selector value (must be list_price or package_quantity)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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