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Label Review Analytics — Record Quality Audit, String Normalisation & Category Frequencies
x402
Label Review Analytics — Record Quality Audit, String Normalisation & Category Frequencies 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 on label review data: record quality audit, string normalisation, and category frequency analysis for up to 200 rows of reviewed records and changed labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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
Three independent analytical reports: (1) a record quality audit flagging completeness, nulls, and anomalies; (2) a string normalisation report standardising text fields like dataset_group; (3) a category frequency table showing distribution counts for categorical fields across the supplied rows. All derived purely from the supplied input data with no external lookups.
Example use cases
ML annotation pipeline quality check
“I have a batch of label review records from our annotation team — about 120 rows covering two dataset groups across October 2026. Can you run a record quality audit, normalise the string fields, and tell me the category frequencies for reviewed_records and changed_labels?”
Label change rate analysis by group
“We ran a labelling sprint across three dataset groups last month. I have the daily reviewed_records and changed_labels counts — around 80 rows total. Can you analyse the category frequencies to see which group had the most label changes, and also flag any data quality issues?”
Pre-training dataset label consistency review
“Before we hand this labelled dataset off to the ML team, I need a string normalisation report and a record quality audit on our 200-row label review export. The rows have dataset_group, date, reviewed_records, and changed_labels fields — can you generate all three reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) bundle of three data quality and normalisation reports on label review data without requiring any external data sources, live predictions, or professional assessments. It is ideal for ML/annotation teams wanting to validate a label review export before downstream use, particularly when the dataset is under 200 rows and you want quality audit, string normalisation, and frequency analysis in a single call.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, reviewed_records, changed_labels) — validation error
- Date fields not matching YYYY-MM-DD pattern — schema validation failure
- Null or out-of-range numeric values for reviewed_records or changed_labels — may produce incomplete audit results
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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