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Validator Uptime — Trailing Mean Series + Date by Category Pivot
x402
Validator Uptime — Trailing Mean Series + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Computes a trailing mean time series and a date-by-category pivot table from supplied validator uptime records (online_minutes, observed_minutes), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Validator uptime — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot for supplied validator uptime records (online_minutes, observed_minutes). 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 reports: (1) a trailing mean time series showing rolling averages of the selected field (online_minutes or observed_minutes) over the specified window, and (2) a date-by-category pivot table cross-tabulating the field values by date and instrument_group. Both reports are computed locally from the supplied rows with no external data lookup.
Example use cases
Validator fleet reliability trending
“I have 120 rows of daily validator uptime data with online_minutes and observed_minutes per group. Can you compute a trailing mean series using a window of 5 on online_minutes and pivot the results by date and instrument group so I can see reliability trends?”
Staking pool uptime audit
“Run a rolling average with a window of 10 on observed_minutes and give me a date-by-category pivot table for these 80 validator uptime records — I want to see which instrument groups are consistently underperforming.”
Multi-group uptime comparison
“I've got validator uptime logs for 4 groups over 3 months — about 150 rows total. Can you generate a trailing mean series (window 14) on online_minutes plus a date-by-category pivot so I can compare uptime across all the groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-12-152?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw validator uptime records (online_minutes, observed_minutes) and need both a rolling/trailing mean time series and a date-by-category pivot in a single low-cost call. It is ideal for offline batch analysis of up to 200 rows without requiring external data sources, live lookups, or prediction models. Prefer alternatives if you need real-time validator data fetching, forecasting, or more than 200 rows.
Side effects
- Fewer than 1 row or more than 200 rows supplied — request rejected
- Window size larger than number of rows — trailing mean series may be empty or produce no output
- Invalid date format (not YYYY-MM-DD) — row rejected or parse error
- Missing required fields (row_id, instrument_group, date, online_minutes, observed_minutes) — validation error
- Numeric values outside allowed range — schema validation failure
- null values in online_minutes or observed_minutes may affect mean calculation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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