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Sampling Observations — Record Quality Audit + Category Frequencies
x402
Sampling Observations — Record Quality Audit + Category Frequencies 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 two local analytical reports on supplied sampling observation rows: a record quality audit and a category frequency breakdown, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + Category frequencies: Record quality audit + Category frequencies 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 malformed fields across the supplied rows, and (2) a category frequency table showing how often each dataset_group value appears in the dataset. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Survey data quality check before analysis
“I have 150 rows of survey sampling data with sample values and weights assigned to different respondent groups. Can you audit the records for quality issues and tell me how frequently each group shows up in the dataset?”
Validating clinical trial sample records
“Before we analyze our trial results, can you run a quality audit on these 80 sampling observation records and also give me a frequency breakdown by study group? Each row has a row_id, date, sample_value, sample_weight, and a dataset_group label.”
Checking market research sample distribution
“We collected sampling observations across four product categories — can you check the record quality and generate a category frequency table so we know if any group is over- or under-represented? I'll supply the rows with sample_value and sample_weight fields.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-006?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and a category frequency summary on a batch of sampling observation records in a single low-cost call. It is ideal for pre-analysis validation of survey, clinical, or market research samples where sample_value and sample_weight are key fields and dataset groups need frequency profiling. It does not perform predictions, external lookups, or professional assessments.
Side effects
- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, dataset_group, date, sample_value, sample_weight) will fail schema validation
- Dates not matching YYYY-MM-DD pattern will be rejected
- Numeric values outside ±1,000,000,000,000 will fail validation
- Empty rows array (minItems: 1 not met) returns an error
- Payment failure results in no report being generated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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