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Data Freshness Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Data Freshness Analytics: Record Quality Audit + String Normalisation + 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 three local analytical reports (record quality audit, string normalisation, and trailing mean series) on up to 200 data freshness rows containing age_minutes and record_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied data freshness records (age_minutes, record_count). 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: (1) a record quality audit identifying data issues in the submitted rows, (2) a string normalisation report for categorical/string fields like dataset_group, and (3) a trailing mean series computed over the selected numeric field (age_minutes or record_count) using the specified window size — all derived purely from the submitted data with no external lookups.
Example use cases
Pipeline freshness quality check
“I have 50 rows of data freshness logs with date, age_minutes, record_count, and dataset_group. Can you audit the record quality, normalize any string fields, and compute a trailing mean over age_minutes with a window of 5?”
Rolling average for staleness monitoring
“Run a trailing mean series with a window of 4 on my record_count field for these data freshness records, and also include the record quality audit and string normalisation reports. I'll send you the rows now.”
Dataset group string cleanup and QA
“I've got about 80 data freshness rows across several dataset groups and I'm worried the group names might be inconsistent. Can you run string normalisation plus a record quality audit and a trailing mean on age_minutes with a window of 3?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, trailing mean) on data freshness records without any external data sources or live lookups. It is ideal for offline/batch analytical workflows on pipeline metadata, data staleness logs, or ETL freshness tracking where cost efficiency matters ($0.01 for all three reports on up to 200 rows). Prefer alternatives if you need predictions, external data enrichment, or more than 200 rows per call.
Side effects
- More than 200 rows submitted — returns an error as the maximum batch size is exceeded
- Missing required fields (row_id, date, age_minutes, record_count, dataset_group) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size outside 2–200 range — rejected by schema
- Field value outside allowed numeric range — schema rejection
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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