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Transformation Throughput Record Quality Audit + IQR Outlier Flags
x402
Transformation Throughput Record Quality Audit + 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 2026-10-28).
Audits data quality and flags IQR-based outliers for transformation throughput records containing processed_rows and duration_seconds fields, delivering two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transformation throughput — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied transformation throughput records (processed_rows, duration_seconds). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a record quality audit identifying issues such as nulls, invalid values, and format problems across the supplied rows; and (2) an IQR-based outlier flagging report identifying which rows contain statistically extreme values for the selected field (processed_rows or duration_seconds). Both reports are generated locally from the supplied data with no external lookups.
Example use cases
ETL pipeline anomaly detection
“I have 120 rows of nightly ETL job metrics with processed_rows and duration_seconds per dataset group — can you audit their data quality and flag any IQR outliers in the duration_seconds field so I know which runs were statistically abnormal?”
Data quality gate before reporting
“Before I publish my weekly pipeline throughput report, can you check these 80 transformation records for data quality issues and outliers in processed_rows? I need both an audit and an IQR outlier report.”
Debugging sparse or null throughput records
“Some of my pipeline records look off — can you run a quality audit and IQR outlier check on these 35 rows of processed_rows and duration_seconds data grouped by dataset_group to find any nulls, bad values, or statistical outliers in the processed_rows field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-13-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and IQR-based outlier detection specifically on transformation throughput metrics (processed_rows, duration_seconds) in a single low-cost call. It is ideal for data engineering workflows, ETL pipeline monitoring, and pre-reporting data validation where you have up to 200 rows and want two independent analytical perspectives without any external data lookups or predictions.
Side effects
- Fewer than 1 row submitted — returns validation error
- More than 200 rows submitted — request rejected due to maxItems limit
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, dataset_group, date) — request rejected
- Invalid field enum value (not processed_rows or duration_seconds) — schema error
- All values null for selected field — IQR computation may be undefined or empty
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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