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Label Review Analytics: Category Frequencies + Trailing Mean Series
x402
Label Review Analytics: Category Frequencies + 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 2026-10-24).
Generates two analytical reports for label review data: category frequency distributions and trailing mean series for reviewed_records or changed_labels metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Category frequencies + Trailing mean series: Category frequencies + 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 reports: (1) a category frequency table showing how often each dataset_group value appears across the supplied rows, and (2) a trailing mean series showing the rolling average of the selected field (reviewed_records or changed_labels) computed over the specified window size, both derived entirely from the supplied data.
Example use cases
Annotation pipeline quality check
“I have 120 rows of label review records from our ML annotation pipeline, each with a date, dataset group, number of reviewed records, and changed labels. Can you compute the category frequencies by dataset_group and a trailing mean over a window of 7 for reviewed_records so I can see which groups are most active and spot any quality trends?”
Label correction rate trend analysis
“Here are 80 label review rows across three dataset groups over the past quarter. Run the two analytics reports — category frequencies and a trailing mean with window 10 for changed_labels — so I can track which groups have the highest correction rates and see if they're improving over time.”
Daily review volume rolling average
“I've got 150 rows of daily label review data and I want to understand the rolling average of reviewed_records over a 5-day window. Can you also show me the frequency breakdown by dataset_group so I know where most of our review effort is concentrated?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-103?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have label review tabular data (up to 200 rows) and need both a category frequency breakdown by dataset_group and a rolling/trailing mean series for a numeric metric in a single low-cost call. It's ideal for MLOps, annotation pipeline monitoring, or data quality review workflows where no external data, live lookup, or prediction is needed — just local analytical summaries of supplied records.
Side effects
- Input array is empty or missing — returns validation error
- Row count exceeds 200 — request rejected
- Window size greater than number of rows — trailing mean may produce null/partial values for early rows
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required row fields (row_id, reviewed_records, changed_labels) — rejected
- Field enum value not recognized — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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