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Data Ingestion Batches — Record Quality Audit & IQR Outlier Flags
x402
Data Ingestion Batches — 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 2026-10-16).
Performs a two-part quality audit on data ingestion batch records: a record quality check and IQR-based outlier flagging for received_rows or accepted_rows fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data ingestion batches — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). 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 reports: (1) a record quality audit identifying issues such as null values, format errors, or constraint violations in the submitted rows, and (2) an IQR-based outlier flagging report identifying which rows have statistically anomalous values in the selected field (received_rows or accepted_rows). Both reports are generated locally with no external lookups.
Example use cases
ETL pipeline row anomaly detection
“I have my daily ETL ingestion logs for last week — can you audit the record quality and flag any IQR outliers in the received_rows field? Here are the batch records for dataset groups alpha and beta across 7 days.”
Accepted row drop investigation
“Something looks off in our accepted_rows counts for the past month — can you run a quality audit and outlier detection on these 50 ingestion batch records across our three dataset groups to see which ones are statistically unusual?”
Multi-group ingestion health check
“I want to sanity-check our data ingestion across four dataset groups before we go to production. Can you run both a record quality report and IQR outlier flags on these batch rows using the accepted_rows field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-01-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a low-cost, self-contained statistical quality check on data ingestion batch records specifically tracking received_rows and accepted_rows metrics. It is ideal when you want both a structural quality audit and IQR outlier detection in a single call without any external data lookups or live predictions. Best suited for ETL monitoring, pipeline validation, and batch data health checks where you have up to 200 rows grouped by dataset and date.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) causes rejection
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- Providing an invalid field enum value (not received_rows or accepted_rows) causes an error
- Empty rows array (minItems: 1 not met) returns an error
- All-null values in the target field may produce degenerate IQR results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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