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Sampling Observations: Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Sampling Observations: Record Quality Audit + String Normalisation + Date by Category Pivot 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 local analytical reports on supplied sampling observation rows: a record quality audit, string normalisation, and a date-by-category pivot for sample_value or sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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 missing, null, or out-of-range values; (2) a string normalisation report cleaning and standardising text fields like dataset_group and row_id; and (3) a date-by-category pivot table aggregating the selected numeric field (sample_value or sample_weight) across dates and dataset groups. All analysis is performed locally with no external lookups.
Example use cases
Survey data quality check
“I have 50 survey sampling rows with row_id, dataset_group, date, sample_value, and sample_weight — can you audit them for quality issues, normalise any strings, and pivot the sample_value by date and category?”
Lab sample weight analysis
“Run all three reports — quality audit, string normalisation, and date pivot — on my 120 lab sampling records, focusing on the sample_weight field. Each row has a row_id, dataset_group, date, sample_value, and sample_weight.”
Environmental monitoring data review
“I've collected 80 environmental sampling observations and want to check data quality, clean up string fields, and see a pivot of sample_value by date and dataset_group — can you do all three in one go?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, date-category pivot) on a small-to-medium batch of sampling observation records (up to 200 rows) in a single low-cost call. Prefer it over general-purpose analytics tools when your data specifically has sample_value and sample_weight fields grouped by dataset_group and date. It is not suitable for live lookups, predictions, or datasets exceeding 200 rows.
Side effects
- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- date field not matching YYYY-MM-DD pattern — rejected with schema validation error
- sample_value or sample_weight outside allowed range (-1e12 to 1e12) — rejected
- row_id missing or empty — rejected
- field enum not one of 'sample_value' or 'sample_weight' — rejected
- Payment not provided or insufficient — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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