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Save Files Analytics: Weighted Mean + Keyed Dataset Comparison
x402
Save Files Analytics: Weighted Mean + 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 2026-10-30).
Computes a secondary-measure weighted mean and compares two keyed save-file datasets (save_kb, write_ms) across game areas, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Secondary-measure weighted mean + Keyed dataset comparison: Secondary-measure weighted mean + 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 reports: (1) a secondary-measure weighted mean of the selected field (save_kb or write_ms) across all supplied rows, weighted by the other measure, and (2) a keyed dataset comparison highlighting matching, differing, and unmatched rows between the primary and secondary input datasets, segmented by game_area.
Example use cases
Save size regression between game builds
“I've got save file records from two different game builds — each row has a game_area, date, save_kb, and write_ms. Run a weighted mean of save_kb and a keyed comparison between the two builds so I can see if save sizes changed between versions.”
Write latency analysis across game areas
“Here are my save file logs from the last sprint, split into two groups. Compute the weighted mean of write_ms using save_kb as the secondary weight, and compare the two datasets by row key so I can spot any game_area outliers.”
QA check on save file performance delta
“I want to QA two snapshots of save file data — primary and comparison sets, up to 150 rows each with save_kb and write_ms. Give me the weighted mean report on write_ms and flag which keyed rows differ between the two snapshots.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-186?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a weighted mean computation and a keyed row-level comparison between two save file datasets in a single call, specifically for game save performance data (save_kb and write_ms). It is purpose-built for game analytics workflows where you want to compare two snapshots or builds without external lookups or predictions — ideal for lightweight, offline analytical reporting at low cost.
Side effects
- Fewer than 1 row provided — validation error
- More than 200 rows supplied — request rejected
- Missing required fields (row_id, game_area, date, save_kb, write_ms) — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Null values in both save_kb and write_ms for a row may produce undefined weighted mean
- Duplicate row_ids may produce ambiguous keyed comparison results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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