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Save Files Daily Sum Series + IQR Outlier Flags
x402
Save Files Daily Sum Series + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Computes daily sum time series and IQR-based outlier flags for save file metrics (save_kb, write_ms) from supplied records
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags 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 independent analytical reports: (1) a daily sum time series aggregating the selected metric (save_kb or write_ms) by date, and (2) IQR-based outlier flags identifying rows or days with anomalous values — all computed locally from the supplied data, up to 200 rows.
Example use cases
Detect bloated save files by date
“I have a list of game save records with dates, game areas, and save sizes in KB — can you compute the daily totals for save_kb and flag any days that look like outliers using IQR? Here are up to 200 rows of data.”
Flag slow write times in game logs
“Can you analyze these game save log entries and give me a daily sum series for write_ms plus flag any rows where write times are outliers based on IQR? I want to catch performance spikes across my game areas.”
Summarize save activity across game zones
“I've got save file records from different game areas over a few weeks — please compute the daily sum series for save_kb and highlight any IQR-flagged outlier days so I can see which zones had abnormal save activity.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-140?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw game save file records (with dates, save sizes, and write times) and need both a daily aggregated time series and statistical outlier detection (IQR-based) in a single low-cost call. It is ideal for gaming analytics pipelines where you want to detect anomalous save behavior without external lookups or predictions — all computation is local and deterministic from your supplied data.
Side effects
- Fewer than 1 or more than 200 rows submitted — rejected by schema validation
- Missing required fields (row_id, game_area, date, save_kb, write_ms) cause row-level errors
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside allowed range rejected
- Invalid field enum value (must be save_kb or write_ms) causes error
- Insufficient data variance may produce trivial or empty IQR outlier results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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