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Label Review Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering
x402
Label Review Analytics — Record Quality Audit, String Normalisation & Numeric Row Ordering 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 on label review data: record quality audit, string normalisation, and numeric row ordering for up to 200 rows of reviewed_records and changed_labels data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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: (1) a record quality audit flagging data issues in the supplied rows, (2) a string normalisation report cleaning dataset_group and related string fields, and (3) a numeric row ordering report ranking rows by the selected field (reviewed_records or changed_labels). All computed locally with no external lookups.
Example use cases
ML annotation pipeline quality check
“I have 120 rows of label review logs with row_id, dataset_group, date, reviewed_records, and changed_labels — can you run a record quality audit, string normalisation, and row ordering on them sorted by changed_labels to spot any data issues before I hand them off to the model team?”
Dataset group string cleanup
“My label review export has inconsistent dataset_group names and I need string normalisation plus a quality audit on up to 80 rows — can you run those three reports on it ordered by reviewed_records?”
Daily label review numeric ranking
“Can you take today's label review batch — about 30 rows across group-a and group-b — and give me a quality audit plus numeric row ordering by reviewed_records so I can see which entries had the most records reviewed?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of label review data and need all three of record quality auditing, string normalisation, and numeric ordering in a single low-cost call ($0.01 USDC). It is ideal for ML data ops teams validating annotation batches before ingestion, where no live prediction, external lookup, or professional assessment is needed — purely local analytical computation on supplied data.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, reviewed_records, changed_labels) causes row-level rejection
- Dates not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 bounds cause schema validation failure
- Empty rows array returns an error
- Invalid field enum value (not reviewed_records or changed_labels) causes a bad request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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