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Feature Availability — Record Quality Audit + Duplicate Key Report
x402
Feature Availability — Record Quality Audit + Duplicate Key Report 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 a record quality audit and duplicate key detection report on supplied feature availability data (populated_features, expected_features) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Duplicate key report: Record quality audit + Duplicate key report 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 reports: (1) a record quality audit highlighting anomalies, missing values, and data integrity issues in the supplied feature availability rows, and (2) a duplicate key report identifying rows with repeated row_id, date, or dataset_group key combinations — all computed locally with no external lookups.
Example use cases
ML feature store data quality check
“I have a batch of feature availability records from our ML pipeline — each row has a row_id, dataset_group, date, populated_features, and expected_features. Can you audit them for quality issues and flag any duplicate keys? There are about 80 rows across three groups.”
Detecting duplicate entries in feature logs
“We've been logging feature availability daily across two dataset groups and I'm worried some rows got inserted twice. Can you run a duplicate key report on these 120 records and also tell me if there are any quality problems?”
Pre-deployment feature data validation
“Before we ship this feature dataset to production, can you do a record quality audit and check for duplicate row IDs? I'll give you around 150 rows with populated_features and expected_features counts by date and group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-003?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, offline-style data quality analysis on feature availability records specifically containing populated_features and expected_features counts — particularly when you want both a general quality audit AND duplicate key detection in a single $0.01 call for up to 200 rows, without needing live lookups or external data enrichment.
Side effects
- Fewer than 1 row provided — request rejected
- More than 200 rows submitted — exceeds batch limit
- Invalid date format not matching YYYY-MM-DD pattern — validation error
- row_id missing or empty — required field error
- populated_features or expected_features outside allowed numeric range — schema rejection
- Malformed JSON body — parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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