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Label Review Analytics: Record Quality Audit + Deduplication + Equal-Width Histogram
x402
Label Review Analytics: Record Quality Audit + Deduplication + Equal-Width Histogram 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: a record quality audit, exact row deduplication, and an equal-width histogram — all computed offline against up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + Exact row deduplication + Equal-width histogram: Record quality audit + Exact row deduplication + Equal-width histogram 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 computed locally against the supplied rows: (1) a record quality audit flagging null values, out-of-range fields, and format violations; (2) an exact row deduplication report identifying and counting duplicate rows; (3) an equal-width histogram for the selected numeric field (reviewed_records or changed_labels) across the specified number of bins. No external lookups or predictions are made.
Example use cases
Annotation quality audit before model training
“Before I kick off training, can you run a record quality audit on these 50 label review rows and tell me if there are any data issues? The rows have columns: row_id, dataset_group, date, reviewed_records, and changed_labels. Use the reviewed_records field for the histogram with 10 bins.”
Deduplication check on merged review batches
“I merged two batches of label review records and I think there might be duplicate rows. Can you run an exact deduplication report and quality check on these 120 rows, plus give me a 5-bin histogram on changed_labels?”
Distribution profiling of changed labels per group
“I want to understand the distribution of changed_labels across my label review dataset — can you run a histogram with 8 bins, a quality audit, and a deduplication check on these 80 records grouped by dataset_group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-219?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, offline, combined data profiling pass — quality audit, deduplication, and histogram — on label review data with up to 200 rows. It is ideal for ML annotation pipelines where you want to validate data before training without making any external API calls or incurring additional per-report costs. At $0.01 USDC for three reports, it is cost-efficient for batch pre-flight checks on small-to-medium label review exports.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) will cause row-level errors
- Invalid date format (not YYYY-MM-DD) will fail schema validation
- Bins value outside 1–30 range will be rejected
- Numeric values outside ±1,000,000,000,000 will fail validation
- Empty rows array (minItems: 1) will be rejected
- Payment not included or insufficient USDC will result in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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