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Label Review Analytics: Record Quality Audit + String Normalisation + Numeric Interval Filter
x402
Label Review Analytics: Record Quality Audit + String Normalisation + Numeric Interval Filter 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 on label review data: record quality audit, string normalisation, and numeric interval filtering for reviewed_records or changed_labels fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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 missing, malformed, or suspect entries; (2) a string normalisation report standardising dataset_group and other string fields; (3) a numeric interval filter report identifying rows where the chosen field (reviewed_records or changed_labels) falls within or outside the specified minimum/maximum range. All computed locally with no external calls.
Example use cases
Annotation pipeline data quality check
“I have a batch of 120 label review records from two dataset groups collected last month — can you audit their quality, normalise the dataset_group strings, and flag any reviewed_records values outside the range 5 to 500?”
Changed labels outlier detection
“Run an interval filter on the changed_labels field for my 80-row annotation review dataset, keeping only values between 0 and 50, and include the quality audit and string normalisation reports too.”
Pre-ingestion label data validation
“Before I load this label review CSV into our warehouse, can you do a full quality audit, normalise the string columns, and filter out any reviewed_records outside 10 to 200 for these 200 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical passes (quality audit, string normalisation, numeric filtering) on label review data in a single $0.01 call, without needing live lookups, ML predictions, or external API calls. Ideal for pre-ingestion validation of annotation or label review datasets up to 200 rows.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — row fails validation
- Missing required field (row_id, reviewed_records, or changed_labels) — row excluded or error returned
- Non-numeric value in reviewed_records or changed_labels — treated as null
- minimum greater than maximum — interval filter may return empty results
- Payment not included or insufficient — 402 response, no reports returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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