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Numeric Precision Comparisons — Row Ordering & IQR Outlier Flags
x402
Numeric Precision Comparisons — Row Ordering & 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).
Ranks numeric rows by value and flags IQR-based outliers for supplied source_value/transformed_value pairs, producing two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — Numeric row ordering + IQR outlier flags: Numeric row ordering + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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
Two independent analytical reports: (1) all submitted rows ordered by the selected numeric field (source_value or transformed_value), and (2) a per-row IQR outlier flag indicating whether each row's value falls outside the interquartile range. Up to 200 rows processed per call.
Example use cases
Outlier detection in sales figures
“I have a list of up to 50 daily sales records with original and adjusted values — can you rank them by source_value and flag any rows that fall outside the IQR as outliers? Here are the rows: row-1 (group-a, 2026-11-01, source=500, transformed=480), row-2 (group-b, 2026-11-02, source=12000, transformed=11800), row-3 (group-a, 2026-11-03, source=510, transformed=490).”
Data pipeline transformation QA
“We ran a numeric transformation on our ETL pipeline and I want to verify the output — please sort these rows by transformed_value and flag any IQR outliers across these 10 records: row-1 (pipeline-A, 2026-09-01, source=100, transformed=95), row-2 (pipeline-A, 2026-09-02, source=102, transformed=5000), row-3 (pipeline-B, 2026-09-03, source=98, transformed=97).”
Sensor reading anomaly detection
“I've got temperature sensor readings with raw and calibrated values for 20 devices — rank them by source_value and show me which ones are statistical outliers using IQR: sensor-1 (zone-north, 2026-08-15, source=22.1, transformed=21.9), sensor-2 (zone-south, 2026-08-15, source=95.3, transformed=94.8), sensor-3 (zone-north, 2026-08-16, source=21.8, transformed=21.7).”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-119?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both numeric row ordering and IQR-based outlier detection on supplied data in a single call, without requiring an external data source or live lookup. It is ideal for data quality checks, ETL validation, or anomaly flagging on small-to-medium datasets (up to 200 rows) where you provide the source and transformed numeric values directly.
Side effects
- Fewer than 1 row submitted — minimum 1 row required
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format — must match YYYY-MM-DD pattern
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Missing required fields (row_id, dataset_group, date) cause validation errors
- Invalid field selector — must be 'source_value' or 'transformed_value'
- All null values for selected field may produce empty or degenerate IQR output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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