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Sampling Observations — Record Quality Audit + Keyed Dataset Comparison
x402
Sampling Observations — Record Quality Audit + 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).
Audits record quality and compares two keyed datasets of sampling observations (sample_value, sample_weight) using local analytical engines, returning two independent reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + Keyed dataset comparison: Record quality audit + Keyed dataset comparison 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
Returns two independent analytical reports: (1) a record quality audit identifying issues such as nulls, out-of-range values, or structural problems in the primary sampling dataset, and (2) a keyed dataset comparison report highlighting differences between the primary and secondary datasets matched by row_id, covering value discrepancies, missing keys, and group-level divergences. All analysis is performed locally with no external lookups.
Example use cases
Survey data quality check
“I have two batches of survey sampling records — each row has a row_id, date, group, sample_value, and sample_weight. Can you audit the quality of the primary batch and compare it against the reference batch to flag any discrepancies? I'll give you up to 80 rows in each.”
A/B test observation comparison
“We ran two versions of a sampling experiment and I have the weighted observations for both — same row IDs, dates ranging from 2025-01-01 to 2025-03-31, values in the hundreds. Can you do a quality audit on the primary set and a keyed comparison against the control set to see where they differ?”
Data pipeline validation
“Before merging our new sampling data into production, can you run a record quality audit on the new batch and compare it row-by-row against the existing dataset? Both have up to 150 rows with sample_value and sample_weight fields and I need to catch any quality issues or value mismatches.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-018?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a standalone data quality audit and a side-by-side keyed comparison of two sampling observation datasets in a single call for $0.01 USDC. It is ideal for validating weighted sample records (sample_value + sample_weight) with up to 200 rows, where no external data lookup, prediction, or professional assessment is needed — pure local analytical computation only.
Side effects
- Exceeding 200 rows in either array returns a validation error
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation error
- Values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Empty rows array (fewer than 1 item) is not accepted
- Payment of 0.01 USDC not completed results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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