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Label Review Analytics — Record Quality Audit, String Normalisation & Numeric Distribution
x402
Label Review 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 2026-10-15).
Runs three analytical reports (record quality audit, string normalisation, and numeric distribution) on up to 200 rows of label review data containing reviewed_records and changed_labels fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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 completeness and structural issues, (2) a string normalisation report identifying inconsistencies or formatting problems in text fields like dataset_group, and (3) a numeric distribution summary (min, max, mean, percentiles, etc.) for the selected numeric field (reviewed_records or changed_labels), all computed locally on the supplied data without any external lookups.
Example use cases
ML annotation pipeline quality check
“I've got 50 rows of label review results from last month's annotation sprint — can you audit the record quality, normalise any string inconsistencies in dataset_group, and show me the numeric distribution of reviewed_records across groups?”
Detecting label change anomalies
“Can you run a numeric distribution report on the changed_labels column across my label review export? I want to see if any dataset groups have unusually high or low label change rates — I have about 120 rows covering October.”
Pre-handoff data quality report
“Before I hand off this label review dataset to the modelling team, can you run all three reports — record quality audit, string normalisation, and numeric distribution — on these 80 rows so I can catch any data issues early?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, low-cost ($0.01) triple analytical report — quality audit, string normalisation, and numeric distribution — on label review tabular data with up to 200 rows, without requiring live lookups, ML predictions, or external data enrichment. It is ideal for pre-processing validation of annotation pipelines or ML label review exports where you want three reports in a single API call.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, date, reviewed_records, changed_labels) in one or more row objects
- Date field does not match YYYY-MM-DD pattern — validation error
- field enum value not recognised (must be reviewed_records or changed_labels)
- Empty rows array — minimum 1 row required
- Numeric values outside allowed range (-1e12 to 1e12) — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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