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Sampling Observations String Normalisation and IQR Outlier Flags
x402
Sampling Observations String Normalisation and 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).
Normalises string fields and flags IQR-based outliers across up to 200 sampling observation rows (sample_value, sample_weight), producing two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied sampling observations records (sample_value, sample_weight). 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 analytical reports covering: (1) string normalisation results for the specified field across all supplied rows, and (2) IQR-based outlier flags identifying which rows contain extreme values in sample_value or sample_weight, scoped to up to 200 input rows.
Example use cases
Survey data outlier audit
“I've got 120 rows of survey sample data with sample_value and sample_weight columns. Can you normalize the string fields and flag any IQR outliers in the sample_value field so I can see which records look anomalous?”
Weighted measurement quality check
“Run IQR outlier detection on my lab measurement observations — I have about 80 rows with dates, dataset groups, and both sample_value and sample_weight. Flag any outliers and give me the two analytical reports, please.”
Multi-group sampling data cleanup
“I need to clean up my sampling records across two dataset groups: group-a and group-b. There are 45 rows total. Can you normalise the strings and identify outliers in the sample_weight field using IQR analysis?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 sampling observation records and need both string normalisation and IQR-based outlier detection in a single low-cost call. Ideal for batch data quality checks on survey, lab, or field measurement datasets where you need two complementary analytical perspectives (string hygiene + statistical outlier flagging) without connecting to external systems or live data sources.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, sample_value, or sample_weight) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- sample_value or sample_weight outside allowed numeric range — rejection
- Invalid field selector (not 'sample_value' or 'sample_weight') — enum validation error
- Payment not completed — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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