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Feature Availability Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report
x402
Feature Availability Analytics — Record Quality Audit, String Normalisation & 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 2026-10-24).
Runs three independent analytical reports (record quality audit, string normalisation, and duplicate key detection) on supplied feature availability records (populated_features vs expected_features) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit highlighting anomalies or missing/invalid values in the supplied rows, (2) a string normalisation report identifying and standardising inconsistent string values (e.g. in dataset_group), and (3) a duplicate key report flagging rows with repeated row_id or key combinations — all computed locally with no external lookups.
Example use cases
Pre-pipeline data quality gate
“Before I load this feature availability dataset into our ML pipeline, can you audit it for record quality issues, normalise any inconsistent string values in dataset_group, and flag any duplicate row IDs? I'll pass you the rows with their dates, populated_features, and expected_features counts.”
Duplicate key investigation in feature store
“I think my feature store export has some duplicate row keys mixed in. Can you run a duplicate key report on these 80 feature availability records — each has a row_id, dataset_group, date, and populated_features and expected_features numbers?”
Monthly feature coverage QA review
“I need to do a quality review of our October feature availability records — can you check them for data quality issues, string normalisation problems in the group labels, and any duplicate keys? I have about 120 rows across two dataset groups with dates and populated vs expected feature counts.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalisation, and duplicate key detection bundled together for feature availability data in a single low-cost call. It is ideal for batch data validation of up to 200 rows before ingestion, ETL quality gates, or periodic QA reviews — especially when no live lookup, prediction, or external enrichment is needed.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values in populated_features or expected_features may affect quality audit completeness
- Payment of 0.01 USDC not included or rejected results in HTTP 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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