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Null Coverage Record Quality Audit with IQR Outlier Flags
x402
Null Coverage Record Quality Audit with 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 2026-10-14).
Performs two analytical reports on up to 200 null-coverage records: a record quality audit and IQR-based outlier flagging for missing_values or total_values fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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 summarizing data issues across the supplied null coverage rows, and (2) IQR-based outlier flags identifying which rows have statistically anomalous values in the selected field (missing_values or total_values). Results are computed locally with no external lookups.
Example use cases
Data pipeline completeness audit
“I have a batch of 120 null coverage records from our ETL pipeline with row IDs, dataset groups, dates, and missing/total value counts — can you run a record quality audit and flag any IQR outliers on the missing_values field so I can catch bad data before it hits production?”
Anomaly detection in dataset group coverage
“We track null coverage across multiple dataset groups daily and I want to know which rows have statistically unusual missing_values counts — can you do an IQR outlier analysis and quality audit on these 80 records grouped by dataset_group?”
Monthly data quality review
“At the end of each month I review our data completeness metrics — can you audit these 200 null coverage records for quality issues and flag any outliers in total_values using IQR so I can spot which dates or groups had abnormal coverage?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a record-level quality audit AND IQR outlier detection on null coverage data in a single call, processing up to 200 rows at once. It is best suited for batch data quality workflows where you have pre-collected missing_values and total_values metrics and want statistical anomaly flags without any external data enrichment or prediction.
Side effects
- Missing required fields (row_id, date, missing_values, total_values) returns validation error
- More than 200 rows submitted exceeds batch limit
- Invalid date format not matching YYYY-MM-DD pattern causes rejection
- Null or out-of-range numeric values outside ±1 trillion boundary fail schema validation
- Empty rows array returns error due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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