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Numeric Precision Comparisons — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Numeric Precision Comparisons — 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 2026-10-15).
Audits up to 200 rows of numeric source/transformed value pairs across three local analytical reports: record quality audit, string normalisation, and IQR-based outlier detection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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 covering: (1) a record quality audit identifying malformed, null, or inconsistent records; (2) a string normalisation report correcting or flagging non-standard string representations; and (3) IQR-based outlier flags identifying statistically anomalous rows in the selected numeric field. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ETL pipeline validation audit
“I just ran my ETL transformation and have 120 rows with source_value and transformed_value columns — can you audit their record quality, normalize any string inconsistencies, and flag which rows look like IQR outliers on the source_value field so I can catch bad transformations?”
Outlier detection in financial data
“Here are 80 financial records with row_id, dataset_group, date, source_value, and transformed_value — please run an IQR outlier check on the transformed_value field, do a record quality audit, and normalize any string issues across the dataset.”
Data quality check before reporting
“Before I send this dataset to the reporting team, can you audit all 200 rows for record quality problems, normalize strings, and use IQR to flag any outlier values in the source_value field so I can clean things up first?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, low-cost ($0.01 USDC) triple-report analytics pass on up to 200 rows of numeric precision comparison data — specifically combining record quality auditing, string normalisation, and IQR outlier detection in one call. Ideal for ETL validation, data pipeline QA, or pre-reporting data hygiene checks where no external data enrichment or prediction is needed.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause validation errors
- Dates not matching YYYY-MM-DD pattern result in schema rejection
- Numeric values outside ±1,000,000,000,000 range are rejected
- Empty rows array returns an error
- Invalid field enum value (anything other than source_value or transformed_value) causes a bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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