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Feature Availability Record Quality Audit + Grouped Numeric Summary
x402
Feature Availability Record Quality Audit + Grouped Numeric Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Runs two analytical reports on supplied feature availability records: a record quality audit and a grouped numeric summary, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Grouped numeric summary: Record quality audit + Grouped numeric summary for supplied feature availability records (populated_features, expected_features). 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 in the supplied feature availability rows, and (2) a grouped numeric summary aggregating the selected field (populated_features or expected_features) by dataset_group. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
ML pipeline feature completeness audit
“I need to audit the data quality of my ML pipeline's feature availability records — can you run both a record quality audit and a grouped numeric summary on these 10 rows across my training and validation groups, analyzing the populated_features field?”
Dataset group comparison for data engineering
“We have feature availability logs across three dataset groups and I want to see a quality audit plus a grouped numeric summary for expected_features — here are 50 rows with row IDs, dates, and group labels, can you analyze them?”
Spotting under-populated feature records
“Can you check my feature availability data for quality issues and give me a summary grouped by dataset group? I'm worried some rows have far fewer populated features than expected — here are 200 rows to analyze using the populated_features field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-005?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two simultaneous analytical reports — a quality audit and a grouped summary — on a batch of up to 200 feature availability records without any external data lookups or live predictions. It is especially useful for ML data ops teams checking feature population completeness across dataset groups on a per-batch basis at low cost ($0.01 per call covering both reports).
Side effects
- Submitting more than 200 rows returns an error — batch must be split
- Rows missing required fields (row_id, dataset_group, date, populated_features, expected_features) will fail schema validation
- Dates not matching YYYY-MM-DD pattern will be rejected
- Null values for both numeric fields on a row may affect report completeness
- Invalid field enum value (anything other than 'populated_features' or 'expected_features') will be rejected
- Payment failure (insufficient USDC or x402 not supported) will prevent execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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