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Label Review — Record Quality Audit + Trailing Mean Series
x402
Label Review — Record Quality Audit + Trailing Mean Series 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 two analytical reports (record quality audit and trailing mean series) on label review data containing reviewed_records and changed_labels fields, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Record quality audit + Trailing mean series: Record quality audit + Trailing mean series for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent analytical reports for the submitted label review rows: (1) a record quality audit flagging completeness, validity, and structural issues per row, and (2) a trailing mean series showing rolling averages of the selected field (reviewed_records or changed_labels) over the specified window size — all computed locally without external lookups.
Example use cases
Label quality monitoring for ML pipeline
“I have 120 rows of daily label review data with reviewed_records and changed_labels across three dataset groups. Can you run a record quality audit and compute a trailing mean with a window of 7 on the reviewed_records field so I can see trends and quality issues?”
Changed labels trend analysis
“Audit the quality of my label review records and give me a trailing mean series over a window of 10 for the changed_labels field — I've got 80 rows covering two dataset groups over the past quarter.”
Annotation throughput review
“I need two reports on my annotation team's label review output: a record quality audit and a 3-day trailing mean for reviewed_records. I have 40 rows with date, dataset_group, reviewed_records, and changed_labels. Can you run both?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-010?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a record quality audit and a trailing mean series on label review data in a single low-cost call. It is ideal for ML teams monitoring annotation quality over time, particularly when data is already available locally and no external lookup, prediction, or professional assessment is needed. Best suited for datasets up to 200 rows with date-stamped, grouped label review metrics.
Side effects
- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, date, reviewed_records, changed_labels) causing validation errors
- Invalid date format (must be YYYY-MM-DD) causing parsing failure
- Window size larger than the number of rows causing incomplete or empty trailing mean series
- Null values in the target field reducing the usable trailing mean window
- Non-numeric values in reviewed_records or changed_labels fields causing computation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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