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Sampling Observations Analytics — Record Quality Audit + String Normalisation + Numeric Distribution
x402
Sampling Observations Analytics — Record Quality Audit + String Normalisation + Numeric Distribution 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 three analytical reports (record quality audit, string normalisation, and numeric distribution) on up to 200 sampling observation rows containing sample_value and sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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
Three independent analytical reports returned together: (1) a record quality audit flagging completeness and validity issues across the supplied rows, (2) a string normalisation report identifying and standardising text fields, and (3) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram buckets) for the selected field (sample_value or sample_weight). All computed locally with no external lookups.
Example use cases
Survey data quality check
“I have a batch of survey sample records with sample_value and sample_weight columns — can you audit the record quality, flag any string formatting issues in the dataset_group labels, and give me a numeric distribution of sample_value across all rows?”
Clinical trial sample validation
“Here are 150 rows from our clinical trial sampling dataset with dates, group labels, and numeric readings — please run a record quality audit plus string normalisation on the group names and a distribution breakdown of the sample_weight field.”
Environmental sensor observation review
“I collected environmental sensor readings stored as sampling observations with row IDs, dates, and group tags — run all three reports (quality audit, string normalisation, and numeric distribution) on sample_value so I can spot any data issues before analysis.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, numeric distribution) on sampling observation data with sample_value and sample_weight fields in a single low-cost call. It is ideal for lightweight, offline data profiling of up to 200 rows without any external network lookups, predictions, or professional assessments. Not suitable for real-time data streaming, large datasets over 200 rows, or tasks requiring external enrichment or prediction.
Side effects
- Exceeding 200 rows causes a validation error — trim input to at most 200 items
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause schema validation failures
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid date format (not YYYY-MM-DD) causes a pattern validation error
- Payment failure via x402 protocol prevents execution
- Empty rows array (minItems: 1 not satisfied) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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