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Feature Availability Analytics — Record Quality Audit + String Normalisation + Numeric Distribution
x402
Feature Availability Analytics — Record Quality Audit + String Normalisation + Numeric Distribution 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 three local analytical reports (record quality audit, string normalisation, and numeric distribution) on up to 200 feature availability records containing populated_features and expected_features fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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 returned together: (1) a record quality audit flagging data issues in the supplied rows, (2) a string normalisation report for string fields like row_id and dataset_group, and (3) a numeric distribution summary for the selected numeric field (populated_features or expected_features), all computed locally without any external lookups.
Example use cases
Feature store completeness check
“I have up to 200 rows of feature availability logs with populated_features and expected_features counts per dataset group and date — can you run a full quality audit, string normalisation, and numeric distribution report on the populated_features field so I can see where data is missing or inconsistent?”
ML pipeline data profiling
“Before we train the next model, can you profile the feature availability records from our last pipeline run? I need a record quality audit, string normalisation, and a numeric distribution breakdown for the expected_features column across all dataset groups.”
Dataset group comparison health check
“We've got feature availability records across group-a and group-b for October — can you run the three analytical reports (quality audit, string normalisation, numeric distribution) on the populated_features field so I can spot any data health issues before we publish?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality and profiling pass on feature availability records (populated vs expected features) in a single $0.01 call, and you want all three analytical reports — quality audit, string normalisation, and numeric distribution — delivered together without any external data lookups, predictions, or side effects. Best suited for ML ops, feature store validation, and ETL pipeline health checks on batches of up to 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — rejected by schema
- Empty rows array — minItems:1 constraint violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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