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Sampling Observations Analytics: Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Sampling Observations Analytics: Record Quality Audit + String Normalisation + Keyed Dataset Comparison 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 sampling observation records (sample_value, sample_weight): a record quality audit, string normalisation, and keyed dataset comparison between two supplied datasets.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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: (1) a record quality audit flagging issues like missing values, out-of-range numbers, or malformed dates; (2) a string normalisation report standardising text fields such as dataset_group; and (3) a keyed dataset comparison report highlighting differences between the primary and secondary supplied datasets — all computed locally with no external lookups.
Example use cases
Survey sample data quality check
“I've collected 150 survey sampling records with sample_value and sample_weight fields and I want to audit them for quality issues, normalise any inconsistent strings in dataset_group, and compare them against last month's dataset to spot differences — can you run all three reports at once?”
A/B test weighted sample comparison
“I have two groups of sampling observations from an A/B test, each with row_id, date, sample_value, and sample_weight — please audit the data quality, normalise the group labels, and compare the two datasets by key to highlight any mismatches.”
Environmental monitoring data validation
“Here are two batches of environmental sampling records (up to 200 rows each) with date, sample_value, and sample_weight — run the record quality audit, string normalisation, and keyed comparison so I can see where the datasets diverge and whether there are any data issues.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously validate record quality, normalise strings, and compare two keyed sampling observation datasets in a single cheap API call (0.01 USDC). It is ideal for batch data quality workflows on datasets up to 200 rows where no live lookups, predictions, or external data are needed — all analysis is performed locally on the supplied data.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors
- Invalid date format (must be YYYY-MM-DD) causes schema rejection
- sample_value or sample_weight outside allowed range (-1e12 to 1e12) causes validation error
- Empty rows array causes error
- Payment of 0.01 USDC not provided via x402 protocol results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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