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Sampling Observations Data Quality Suite: Record Audit + String Normalisation + Exact Deduplication
x402
Sampling Observations Data Quality Suite: Record Audit + String Normalisation + Exact Deduplication 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).
Performs three combined data-quality operations on up to 200 sampling observation rows: a record quality audit, string field normalisation, and exact row deduplication — all processed locally without external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 submitted sampling observation rows: (1) a record quality audit identifying missing values, out-of-range figures, or malformed fields; (2) a string normalisation report standardising text fields like dataset_group; and (3) an exact row deduplication report identifying identical records. All processing is local — no external lookups or predictions are included.
Example use cases
Pre-analysis survey data cleanup
“Before I run my weighted analysis, can you audit these 50 survey sampling rows for quality issues, normalise any inconsistent string labels in dataset_group, and flag any exact duplicate rows? Here are the records with sample_value and sample_weight fields.”
Deduplication check on merged observation exports
“I merged two observation exports and I'm worried there are duplicate rows — can you run an exact deduplication check plus a record quality audit on these 120 rows? Each row has a row_id, date, dataset_group, sample_value, and sample_weight.”
Routine QA on weekly sampling batch
“Run the weekly QA routine on this batch of sampling observations — I need the record quality audit, string normalisation, and exact row deduplication reports for these 80 rows covering group-a and group-b from October 2026.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-effective data quality pass over sampling observation records (with sample_value and sample_weight fields) and want all three reports — quality audit, string normalisation, and exact deduplication — in a single call for up to 200 rows. It is ideal for pre-analysis data cleaning pipelines where no external data enrichment or live prediction is needed and the dataset fits within the 200-row limit.
Side effects
- Submitting more than 200 rows returns a validation error
- Rows missing required fields (row_id, date, sample_value, sample_weight, dataset_group) are rejected
- Dates not matching YYYY-MM-DD pattern cause schema validation failure
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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