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Sampling Observations — Record Quality Audit + String Normalisation
x402
Sampling Observations — Record Quality Audit + String Normalisation 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).
Audits data quality and normalises strings in sampling observation records (sample_value, sample_weight) for up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation: Record quality audit + String normalisation 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 independent reports: (1) a record quality audit identifying issues such as nulls, out-of-range values, or malformed rows across the supplied sampling observations; (2) a string normalisation report detailing inconsistencies, whitespace problems, or casing issues in string fields like dataset_group. Both reports are generated locally from the supplied data with no external lookups.
Example use cases
Pre-analysis sampling data cleanup
“I've got 120 rows of survey sampling data with sample_value and sample_weight columns and I need to check them for quality issues and normalise the strings before I run my weighted analysis — can you audit them and give me two reports?”
Detecting bad records in weighted observations
“Before I import this batch of 80 sampling observations into my pipeline, can you run a record quality audit on the sample_value and sample_weight fields and also normalise any messy strings in the dataset_group names?”
Validating grouped sample data by date
“I have sampling observation records across several dataset groups from October 2026, around 50 rows, and I need a quality audit plus string normalisation report to catch any nulls, outliers, or formatting problems before sharing the results with my team.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, local, two-report quality assessment of sampling observation records specifically containing sample_value and sample_weight fields, for up to 200 rows, with no need for live lookups, predictions, or external data enrichment. Prefer it over sibling endpoints when you only need record quality audit and string normalisation (without grouped numeric summary, deduplication, or correlation analysis).
Side effects
- More than 200 rows submitted — request rejected with maxItems validation error
- Missing required fields (row_id, date, sample_value, sample_weight, dataset_group) — row-level validation failure
- Date field not matching YYYY-MM-DD pattern — schema validation error
- Numeric values outside allowed range (-1e12 to 1e12) — validation rejection
- Empty rows array — minItems constraint failure
- Payment not completed via x402 — request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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