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Save Files Analytics — Trailing Mean Series + Keyed Dataset Comparison
x402
Save Files Analytics — Trailing Mean Series + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Computes a trailing mean time series and a keyed dataset comparison for up to 200 save-file records, reporting on save_kb or write_ms metrics across game areas.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Trailing mean series + Keyed dataset comparison: Trailing mean series + Keyed dataset comparison 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
Returns two analytical reports: (1) a trailing mean time series showing the rolling average of the selected metric (save_kb or write_ms) over the supplied date-ordered rows, and (2) a keyed dataset comparison highlighting differences between the primary and secondary datasets grouped by game_area. Up to 200 rows are supported per dataset.
Example use cases
Compare save sizes across two game builds
“I have two batches of save file records from our game — one from build v1 and one from build v2, each with about 80 rows including game_area, date, save_kb, and write_ms. Can you run a keyed comparison and trailing mean series with a window of 5 on the save_kb field to see how save sizes changed between builds?”
Spot write latency trends by game area
“I've got 120 daily save file records tagged by game_area and date, and I want to see a rolling average of write_ms with a window of 7 days, plus compare them against a baseline dataset of the same size — can you do that analysis on the write_ms field?”
Audit game area save performance over a month
“Here are two months of save file data for our RPG, about 60 rows each with row_id, game_area, date, save_kb, and write_ms. Please generate a trailing mean series and keyed dataset comparison using a window of 4 on the save_kb metric so I can see which game areas are growing in save size.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-153?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a rolling/trailing mean time series and a side-by-side keyed comparison of two save-file datasets in a single call, specifically for game save metrics (save_kb, write_ms) segmented by game_area. It is well-suited for game developers or QA pipelines that want statistical trend analysis and dataset divergence detection without setting up their own analytics infrastructure. Not appropriate for real-time monitoring, prediction, or datasets exceeding 200 rows.
Side effects
- Missing required row fields (row_id, game_area, date, save_kb, write_ms) causes validation error
- More than 200 rows submitted results in rejection
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Invalid field enum value (not save_kb or write_ms) causes rejection
- Empty rows array causes error
- Payment not included or insufficient USDC causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| window | integer |
Tags
x402
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