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Label Review Analytics — Record Quality Audit, String Normalisation & Base-100 Series
x402
Label Review Analytics — Record Quality Audit, String Normalisation & Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three independent analytical reports (record quality audit, string normalisation, and base-100 index series) on up to 200 rows of label review data covering reviewed_records and changed_labels fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied label review records (reviewed_records, changed_labels). 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
Returns three independent analytical reports for the submitted label review rows: (1) a record quality audit flagging completeness and consistency issues, (2) a string normalisation report standardising text fields such as dataset_group, and (3) a base-100 index series for the selected numeric field (reviewed_records or changed_labels) rebased from the specified row index. All analysis is performed locally on supplied data with no external lookups.
Example use cases
Annotation quality check for ML team
“I have 50 rows of label review logs from our annotation pipeline across two dataset groups from last quarter — can you run the record quality audit, string normalisation, and base-100 series reports on them, using reviewed_records as the target field and starting the base index at row 0?”
Label change rate trend analysis
“Can you run the three label review analytics reports on our changed_labels data for November 2026 — I want the quality audit, string normalisation, and a base-100 index series rebased from row 0 so I can see the trend over the month across our dataset groups?”
Data readiness check before model retraining
“Before we kick off retraining, I need to verify our label review records are clean — please run the record quality audit, string normalisation, and base-100 series on these 120 rows of reviewed_records data grouped by dataset_group, starting the base index at position 0.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical passes — quality audit, string normalisation, and base-100 indexing — on label review data in a single low-cost call. It is ideal for ML/annotation teams validating batches of up to 200 reviewed_records and changed_labels rows before downstream processing. Prefer it over separate single-report endpoints when you want all three report types together for $0.01 USDC without external lookups or predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Null or out-of-range numeric values in reviewed_records or changed_labels may produce quality flags or errors
- base_index referencing a row beyond the dataset length fails validation
- Missing required fields (row_id, date) causes request rejection
- Payment failure or insufficient USDC balance blocks the call
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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