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Label Review Data Quality Audit — Record Quality Audit, String Normalisation & Missing-Value Fill
x402
Label Review Data Quality Audit — Record Quality Audit, String Normalisation & Missing-Value Fill 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).
Audits label review records for data quality issues, normalises string fields, and fills missing values using zero/mean/median imputation for up to 200 rows across reviewed_records and changed_labels fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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 anomalies, duplicates, or structural issues; (2) a string normalisation report showing standardised values for text fields like dataset_group; and (3) a missing-value fill report with imputed values for the chosen numeric field (reviewed_records or changed_labels) using the selected method (zero, mean, or median). All processing is local — no external lookups or predictions.
Example use cases
Pre-training annotation data cleanup
“I have 120 label review records from our annotation pipeline — can you run a full quality audit, normalise the dataset_group strings, and fill any missing reviewed_records values using median imputation before I hand them off to model training?”
Missing changed_labels imputation
“Several rows in my label review batch have null changed_labels values. Can you run the missing-value fill report using mean imputation on the changed_labels field for these 80 records?”
Routine quality check on daily review log
“Here are today's label review logs — 40 rows with dates, dataset groups, and record counts. Can you do the three-report quality audit including string normalisation and zero-fill for any missing reviewed_records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) batch audit of label review records covering quality checks, string normalisation, and missing-value imputation in a single call. It is ideal for annotation pipeline QA workflows where you want three standardised reports without external API calls, live predictions, or professional review. Best for datasets up to 200 rows with the specific reviewed_records and changed_labels schema.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Empty rows array returns an error
- Invalid enum values for field or method parameters cause schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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