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Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three local analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of numeric precision comparison data containing source and transformed values.
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied numeric precision comparisons records (source_value, transformed_value). 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 computed locally on the supplied rows: (1) a record quality audit flagging completeness and anomalies, (2) a string normalisation report standardising text fields, and (3) a date-by-category pivot summarising numeric values grouped by date and dataset_group. All analysis is performed on the submitted data with no external lookups.
Example use cases
ETL pipeline transformation audit
“I just ran an ETL job that transformed our source numeric values and I want to check the quality. Can you audit up to 150 rows of my source_value vs transformed_value records, normalise any string fields, and give me a pivot by date and category so I can spot where the transformation went wrong?”
Data quality check on grouped records
“We have two dataset groups — 'group-a' and 'group-b' — and I need a record quality audit plus a date-by-category pivot on about 80 rows of our precision comparison data. Each row has a date, row_id, source_value, and transformed_value. Can you run all three reports at once?”
Vendor-supplied data normalisation review
“A vendor sent us 120 rows of numeric comparison data with source and transformed values dated across October 2026, and I need to check record quality, normalise the string fields, and see a pivot of the values by date and category group before we load it into our system.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — record quality audit, string normalisation, and date-by-category pivot — on a batch of up to 200 numeric precision comparison rows in a single low-cost call. It is ideal for ETL validation, data quality checks before ingestion, or post-transformation audits where source and transformed values need to be compared. Prefer it over single-report endpoints when all three report types are relevant and cost efficiency matters. Not suitable when you need live data lookup, predictions, or external integrations.
Side effects
- Empty or missing rows array returns a validation error
- More than 200 rows submitted — exceeds maxItems limit
- Row missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 range are rejected
- Invalid field enum value (not 'source_value' or 'transformed_value') causes rejection
- Payment of 0.01 USDC not settled — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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