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Sampling Observations — Record Quality Audit + Base-100 Series
x402
Sampling Observations — Record Quality Audit + Base-100 Series 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).
Runs two local analytical reports on sampling observation rows: a record quality audit and a Base-100 index series, for up to 200 rows of sample_value/sample_weight data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + Base-100 series: Record quality audit + Base-100 series 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 missing values, out-of-range entries, or malformed records across the submitted rows; and (2) a Base-100 index series normalizing the selected field (sample_value or sample_weight) relative to the specified base row, showing relative change across the dataset. Both reports are computed locally with no external lookups.
Example use cases
Survey data quality check
“I have 120 rows of survey sampling data with sample_value and sample_weight fields — can you run a record quality audit on them and also generate a Base-100 index series anchored to the first row so I can see how values shifted over time?”
A/B test sample normalization
“I've got two groups of sampling observations (group-a and group-b) across 80 rows with dates, sample values, and weights. Run both the quality audit and the Base-100 series on sample_weight, using row index 0 as the base, so I can compare group trends.”
Pipeline data validation for sampling logs
“Before I load these 200 sampling observation records into our data warehouse, can you audit them for record quality issues and produce a Base-100 series on sample_value starting from row index 0? I want to catch any bad data and see the normalized trend.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-012?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two bundled analytical outputs — a data quality audit and a Base-100 index series — on sampling observation data (up to 200 rows) in a single low-cost call ($0.01 USDC). It is ideal when you want to validate record completeness and normalize a numeric series simultaneously without relying on external APIs or live data sources. Prefer alternatives if you need deduplication, histogram analysis, string normalization, or more than 200 rows.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause row rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- base_index out of range (0–199) returns an error
- Values outside ±1,000,000,000,000 fail schema bounds
- Empty rows array (minItems: 1 not met) returns a bad request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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