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Feature Availability Analytics — Record Quality Audit, String Normalisation & Adjacent Row Changes
x402
Feature Availability Analytics — Record Quality Audit, String Normalisation & 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 three independent analytical reports (record quality audit, string normalisation, and adjacent row change detection) 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 + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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 highlighting issues such as nulls, out-of-range values, or missing fields in the feature availability rows; (2) a string normalisation report identifying and standardising inconsistent string representations; (3) an adjacent row change report detecting significant differences between consecutive rows in the chosen numeric field (populated_features or expected_features). All reports are computed locally with no external lookups.
Example use cases
ML feature store completeness audit
“I have a batch of 150 feature availability records from our ML pipeline with populated_features and expected_features counts per date and dataset group — can you audit their record quality, normalise any string fields, and flag where adjacent rows show unexpected changes?”
Daily data pipeline quality check
“Run the record quality audit, string normalisation, and adjacent row change detection on these 80 feature availability rows from our daily ETL run — I need to spot any gaps between what's populated versus what was expected.”
Dataset group feature coverage review
“I've got feature availability records across three dataset groups covering October 2026 — please check them for quality issues, normalise the strings, and tell me where there are notable changes between consecutive rows in the populated_features field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data quality reports — record quality audit, string normalisation, and adjacent row change detection — on feature availability data (populated vs expected feature counts) in a single low-cost call. It is best suited for batch sizes up to 200 rows and situations where no live lookup, external enrichment, or predictive modelling is required. It is more cost-efficient than calling separate audit, normalisation, and change-detection services individually.
Side effects
- Submitting more than 200 rows will be rejected (maxItems constraint)
- Missing required fields (row_id, date, populated_features, expected_features) will cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation
- Numeric values outside the allowed range (-1e12 to 1e12) will be rejected
- Payment failure or insufficient USDC balance will prevent the call from completing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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