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Feature Availability Record Quality Audit and Adjacent Row Changes
x402
Feature Availability Record Quality Audit and Adjacent Row Changes 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 adjacent row change detection, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes 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 for the submitted feature availability rows: (1) a record quality audit flagging completeness and consistency issues across populated_features and expected_features, and (2) an adjacent row changes report showing deltas or anomalies between consecutive rows. Both reports are computed locally with no external lookups.
Example use cases
ML pipeline feature completeness check
“I have a batch of 50 feature availability records from my ML pipeline across three dataset groups — can you audit the record quality and flag any adjacent row changes in the populated_features column for dates from 2026-09-01 through 2026-09-20?”
Dataset group drift detection
“Run an adjacent row changes and quality audit on these feature availability rows for group-b — I want to see where expected_features jumped or dropped unexpectedly between consecutive dates in October 2026.”
Data ingestion validation for analytics team
“We just ingested a new batch of feature records for group-alpha and I need both a quality audit and adjacent row change report on the populated_features field before we push to production — can you process these 150 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-011?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two simultaneous analytical passes — quality audit plus sequential change detection — on a batch of feature availability records without any external data lookups or live predictions. It is ideal for validating ML dataset completeness and spotting row-level drift in a single low-cost call covering up to 200 rows.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern cause input rejection
- Null values in both populated_features and expected_features may limit audit depth
- Invalid field selector enum value causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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