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Numeric Precision Comparisons — IQR Outlier Flags + Keyed Dataset Comparison
x402
Numeric Precision Comparisons — IQR Outlier Flags + Keyed Dataset Comparison 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 two statistical reports on supplied numeric rows: IQR-based outlier flagging and keyed dataset comparison between a primary and alternate dataset (up to 200 rows each).
Price / call$0.01
Success—
StatusUnknown
How this service works
Numeric precision comparisons — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison 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
Returns two independent analytical reports: (1) an IQR outlier flagging report identifying which rows fall outside the interquartile range for the selected numeric field, and (2) a keyed dataset comparison report highlighting differences between the primary and alternate datasets matched by row_id. Both reports are computed locally with no external lookups.
Example use cases
ETL pipeline transformation validation
“I ran a data transformation on my sales pipeline and want to verify nothing went crazy — here are my 150 rows with original and transformed revenue figures. Can you flag any IQR outliers in source_value and also compare the primary dataset against the alternate version I have from last week's run, keyed by row ID?”
Detecting anomalies in financial records
“I have 80 rows of transaction amounts, each with a source_value and a transformed_value after normalization. Please run the IQR outlier check on source_value and also do a keyed comparison between my current dataset and a prior snapshot to see what changed.”
QA check on ML feature engineering
“Before I feed this into my model, can you check my 60 engineered feature rows for outliers using IQR on the transformed_value field, and also compare the primary feature table against the baseline dataset by row ID to highlight any discrepancies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-20-179?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to run both IQR outlier detection and a keyed dataset comparison in a single low-cost call on up to 200 rows of numeric data. It is ideal for data quality checks on ETL pipelines, before-and-after transformation validation, and statistical anomaly detection without requiring external data sources. It is not suitable for real-time streaming analysis, predictions, or datasets exceeding 200 rows.
Side effects
- Rows array is empty or missing — returns validation error
- More than 200 rows submitted — request rejected
- source_value or transformed_value outside ±1,000,000,000,000 — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- row_id missing or exceeds 80 characters — rejected
- field enum value not 'source_value' or 'transformed_value' — validation error
- Mismatched row_ids between primary and alternate datasets may result in empty or partial comparison report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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