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Data Freshness Record Quality Audit + Trailing Mean Series
x402
Data Freshness 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 2026-10-19).
Runs two analytical reports—a record quality audit and a trailing mean series—on supplied data freshness records (age_minutes and record_count fields), returning local analytics with no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Record quality audit + Trailing mean series: Record quality audit + Trailing mean series for supplied data freshness records (age_minutes, record_count). 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
Returns two analytical reports: (1) a record quality audit that flags issues, anomalies, or quality signals in the supplied freshness records; and (2) a trailing mean series showing rolling averages over the specified window for the chosen field (age_minutes or record_count), all computed locally without external lookups.
Example use cases
Pipeline freshness monitoring audit
“I have a list of daily data pipeline records with age_minutes and record_count — can you run a record quality audit and a trailing mean series on age_minutes with a window of 5 to spot any quality issues or trends?”
Dataset group freshness comparison
“Can you audit my data freshness records for two dataset groups — 'warehouse' and 'retail' — and give me a trailing mean of record_count over a window of 7 days so I can compare how fresh each group's data has been?”
Data ingestion quality check
“I've got about 150 rows of ingestion log records, each with a date, age_minutes, and record_count. Please run both the record quality audit and trailing mean series on record_count using a window of 4 and tell me where quality drops off.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-010?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two combined analytical reports—quality audit and trailing mean—on data freshness records in a single low-cost call, without requiring any external data lookup, live prediction, or professional assessment. It is especially suited for monitoring data pipeline freshness metrics (age_minutes, record_count) across dataset groups over time, and for lightweight batch analysis of up to 200 rows at $0.01 per call.
Side effects
- Fewer than 1 row supplied returns a validation error
- More than 200 rows supplied exceeds the row limit and is rejected
- Window size larger than number of rows may yield empty or partial trailing mean series
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, dataset_group, date) cause request rejection
- Non-numeric values in age_minutes or record_count outside the allowed range cause errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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