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Sampling Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Sampling Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes 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 up to 200 sampling observation rows: record quality audit, string normalisation, and adjacent row change detection for sample_value or sample_weight.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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 generated locally from the supplied rows: (1) a record quality audit flagging malformed, missing, or out-of-range values; (2) a string normalisation report identifying inconsistent casing, whitespace, or formatting in text fields like dataset_group; and (3) an adjacent row changes report showing where consecutive rows differ significantly in the selected numeric field (sample_value or sample_weight). No external data is fetched.
Example use cases
Survey data quality pre-check
“Before I analyse my survey results, can you audit record quality, check string normalisation, and flag adjacent row changes for these 150 sampling observation rows? The field I care about is sample_value.”
Weighted sample anomaly detection
“I've got 80 rows of weighted sample observations and I want to know if there are any quality issues, string inconsistencies in the dataset_group labels, and sudden jumps between consecutive sample_weight values — can you run all three reports?”
Time-series observation consistency review
“These 200 daily sampling records go from 2025-01-01 to 2025-07-20 — can you do a record quality audit, normalise the string fields, and show me where adjacent rows have big changes in sample_value?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical reports (quality audit, string normalisation, adjacent row changes) on sampling observation data in a single low-cost call, without requiring any external lookups, predictions, or live data. It is ideal for pre-analysis data validation pipelines where sample_value and sample_weight are the core numeric fields.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) on any row — validation error
- Invalid date format (must match YYYY-MM-DD) — pattern validation failure
- Field selector not one of 'sample_value' or 'sample_weight' — enum error
- Payment not supplied or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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