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Label Review Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report
x402
Label Review Analytics — Record Quality Audit, String Normalisation & Duplicate Key Report 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 local analytical reports (record quality audit, string normalisation, and duplicate key detection) 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 + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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 missing, out-of-range, or malformed field values; (2) a string normalisation report identifying casing, whitespace, or formatting inconsistencies in text fields like dataset_group; and (3) a duplicate key report listing any repeated row_id values. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
ML pipeline label audit
“I have 120 rows of label review records from our NLP annotation pipeline with dataset groups 'sentiment' and 'ner', dates ranging from 2026-09-01 to 2026-09-30. Can you run the quality audit, string normalisation, and duplicate key report on all of them so I can catch any bad rows before we train?”
Duplicate key detection for merged datasets
“We merged two label review exports into one file and I'm worried there are duplicate row IDs. Can you run the duplicate key report alongside the record quality audit on these 80 rows — dataset groups are 'batch-A' and 'batch-B', all dated 2026-10-15?”
String normalisation check for labelling output
“My labelling team uses inconsistent casing and spacing in the dataset_group field across 60 label review records from 2026-11-01 to 2026-11-07. Can you run the string normalisation and quality audit reports on those rows so I know what needs cleaning?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of label review data and need all three analytical reports (quality audit, string normalisation, duplicate detection) in a single low-cost call ($0.01 USDC). It is ideal for ML/data teams validating annotation pipeline outputs before training, or for anyone needing a bundled data hygiene check on label review records without requiring live lookups, predictions, or external data enrichment.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause row-level validation errors
- Malformed date strings not matching YYYY-MM-DD pattern rejected by schema validation
- Numeric values outside the allowed range (±1,000,000,000,000) cause schema rejection
- Empty rows array returns an error
- Service unavailable or payment not processed results in 402 or 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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