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Sampling Observations — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Sampling Observations — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean 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).
Runs three local analytical reports on supplied sampling observation rows: a record quality audit, string normalisation, and a secondary-measure weighted mean calculation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied sampling observations records (sample_value, sample_weight). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports for the supplied sampling observation rows: (1) a record quality audit flagging data issues, (2) string normalisation results for text fields, and (3) a secondary-measure weighted mean computed from sample_value weighted by sample_weight. All analysis is performed locally with no external lookups.
Example use cases
Survey data quality check
“I've got 50 rows of survey sampling observations with sample_value and sample_weight fields — can you run a quality audit to flag any bad records, normalise the string fields, and calculate a weighted mean of the sample values across all rows?”
Weighted mean for A/B test samples
“I have two groups of sampling observations from our A/B test (group-a and group-b), each with sample values and weights. Please audit the record quality, normalise any string fields, and give me the secondary-measure weighted mean for the sample_value field across up to 80 rows.”
Pre-analysis data cleaning for sampling dataset
“Before I analyse this dataset, can you do a record quality audit, string normalisation, and weighted mean computation on these 120 sampling observation rows? Each row has a row_id, date, dataset_group, sample_value, and sample_weight.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality audit, string normalisation, and weighted mean — on sampling observation data in a single cost-efficient call. Ideal when you have up to 200 rows of data with sample_value and sample_weight fields and want offline local computation without any external API calls or predictions.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — schema validation error
- sample_value or sample_weight out of allowed numeric range — validation failure
- Missing required fields (row_id, dataset_group, date) — schema rejection
- Invalid field enum value — request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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