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Data Validation Results - IQR Outlier Flags
x402
Data Validation Results - 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 2026-10-24).
Detects IQR-based statistical outliers in data validation result records (checked/failed record counts) across rules and dates
Price / call$0.01
Success—
StatusUnknown
How this service works
Data validation results - IQR outlier flags: IQR outlier flags for Data validation results. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.
Output
A bounded report identifying which input rows are IQR outliers for the selected numeric field (checked_records or failed_records), with outlier flags per row based on interquartile range statistics computed locally across the submitted dataset.
Example use cases
ETL pipeline anomaly detection
“I have daily validation logs from my ETL pipeline — each row has a rule name, a date, and counts of checked and failed records. Can you flag any rows where the failed_records count is a statistical IQR outlier across the batch?”
Data quality rule performance audit
“We run about 15 different data quality rules every day. Can you analyze the past week's checked_records counts and tell me which rules are outliers — ones that are processing abnormally high or low record volumes compared to the rest?”
Spike detection in failed validation records
“I'm monitoring our nightly data validation jobs and I want to catch unexpected spikes before they become incidents. Here are the last 30 days of rule results — please use IQR outlier detection on the failed_records field and tell me which rows are flagged.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-validation-results-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local IQR-based outlier detection on data validation metrics (checked/failed record counts) without sending data to an external enrichment or forecasting service. Ideal for ETL monitoring, data quality audits, and pipeline anomaly detection on batches up to 200 records at a low fixed cost.
Side effects
- Too few rows (minimum 1, but IQR meaningful only with sufficient data points)
- Invalid date format (must match YYYY-MM-DD pattern)
- Field value out of allowed numeric range (±1,000,000,000,000)
- Exceeding 200 row limit returns validation error
- Missing required fields (row_id, rule, date, checked_records, failed_records) causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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