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Save Files Analytics — Numeric Row Ordering + Trailing Mean Series
x402
Save Files Analytics — Numeric Row Ordering + 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).
Analyzes save file metrics (save_kb, write_ms) by computing numeric row ordering and trailing mean series for up to 200 supplied rows, returning 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Numeric row ordering + Trailing mean series: Numeric row ordering + Trailing mean series for supplied save files records (save_kb, write_ms). 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: (1) a numeric row ordering of the supplied save file rows, and (2) a trailing mean series for the selected field (save_kb or write_ms) computed with the specified sliding window size. Both reports are derived entirely from the supplied data with no external lookups.
Example use cases
Rolling average of save file sizes
“I have 10 game save records with dates, save sizes in KB, and write times in milliseconds — can you compute the numeric row ordering and a trailing mean for save_kb using a window of 3?”
Write latency trend analysis for game areas
“Can you rank these 50 save file records by row and calculate a rolling mean of write_ms with a window size of 5 so I can see if write latency is trending up across game areas?”
Session save performance comparison
“I want to compare save performance across two game areas — run numeric ordering and a trailing mean series on write_ms with a window of 4 across these 20 save session records.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-115?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have save file performance records (save_kb, write_ms) and need both a ranked/ordered view of the data and a smoothed rolling average trend in a single call for under 200 rows. It is ideal for game developers or analysts who want offline statistical summaries without external data dependencies, predictions, or professional assessments.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum limit
- Invalid date format (must be YYYY-MM-DD) — validation error
- Window size larger than number of rows — trailing mean may be undefined for early rows
- Missing required fields (row_id, game_area, date) — schema validation failure
- Numeric field values outside ±1,000,000,000,000 range — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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