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Record Routing — Record Quality Audit + IQR Outlier Flags
x402
Record Routing — 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 2026-10-15).
Runs a record quality audit and IQR-based outlier detection on up to 200 rows of record routing data (accepted_records and rejected_records), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied record routing records (accepted_records, rejected_records). 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 data issues such as nulls, invalid values, or structural problems across the supplied rows, and (2) IQR-based outlier flags identifying which rows contain statistically anomalous values for the selected field (accepted_records or rejected_records). No external lookups or predictions are made; all analysis is performed locally on the supplied data.
Example use cases
Spotting outliers in nightly routing batch
“I ran my nightly record routing job and got 150 rows of accepted and rejected counts per group — can you audit the data quality and flag any IQR outliers in the accepted_records field so I know which rows look suspicious?”
Validating routing pipeline before reporting
“Before I send my weekly routing report, can you do a quality audit and IQR outlier check on these 80 rows of routing data across my dataset groups, looking at the rejected_records field to catch anything anomalous?”
Investigating data spikes in routing logs
“I think something went wrong in our routing pipeline last month — can you audit quality and flag statistical outliers using IQR on these 200 rows of accepted_records data grouped by dataset_group and date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of record routing data and need both a structured quality audit and IQR-based outlier detection in a single low-cost call. It is ideal for pipeline validation, pre-reporting checks, and anomaly investigation on routing acceptance/rejection counts. Prefer this over general analytics endpoints when your data specifically has the accepted_records and rejected_records structure and you want two reports for $0.01 USDC without any external data lookups.
Side effects
- Rows exceed 200-item limit — request is rejected
- Missing required fields (row_id, date, accepted_records, rejected_records) — validation error
- date field not in YYYY-MM-DD format — schema validation failure
- field enum value not one of accepted_records or rejected_records — error
- Empty rows array — rejected due to minItems:1 constraint
- Numeric values outside ±1,000,000,000,000 range — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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