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Feature Availability — Record Quality Audit + Daily Sum Series
x402
Feature Availability — Record Quality Audit + Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs two local analytical reports on feature availability data: a record quality audit and a daily sum series, for up to 200 rows of populated/expected feature counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Daily sum series: Record quality audit + Daily sum series 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 data issues, nulls, or anomalies in the supplied feature availability rows, and (2) a daily sum series aggregating the selected field (populated_features or expected_features) by date — all computed locally without external lookups, covering up to 200 rows.
Example use cases
ML pipeline feature coverage audit
“I have a dataset of feature availability records from my ML pipeline — each row has a date, dataset group, and counts of how many features were populated vs expected. Can you run a record quality audit and daily sum series on populated_features for all 120 rows to see where we're missing coverage?”
Daily feature completeness trend report
“I want to understand how our expected_features count changes day by day across our data groups. Can you generate a daily sum series and a quality audit report on my expected_features field across these 80 rows of feature availability data?”
Data observability check for feature store
“Our feature store team wants to check for quality issues in last month's feature availability logs — about 150 rows with dates, group names, and populated vs expected feature counts. Can you run the record quality audit and daily sum series on the populated_features column?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-009?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two simultaneous analytical reports — a quality audit and a daily aggregation — on feature availability data with populated/expected feature counts, all processed locally without any external API calls or live data lookups, and when your dataset is 200 rows or fewer. It is especially suited for ML data pipeline monitoring, feature store observability, and lightweight data quality checks at low cost ($0.01 USDC for both reports).
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern) causes row rejection
- Null values in numeric fields may reduce accuracy of sum series
- field enum value not one of 'populated_features' or 'expected_features' causes error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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