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Simulation Caches IQR Outlier Flags + Explicit Missing-Value Fill
x402
Simulation Caches IQR Outlier Flags + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).
Runs two local analytical reports on simulation cache records: IQR-based outlier detection and explicit missing-value imputation for cache_gb and simulation_seconds fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied simulation caches records (cache_gb, simulation_seconds). 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 per call: (1) IQR-based outlier flags indicating which rows fall outside the interquartile range for the specified field, and (2) a missing-value fill report showing which null values were imputed and what values were substituted (zero, mean, or median). Results cover up to 200 input rows and are computed locally without any external lookups.
Example use cases
Outlier audit on cache storage data
“Here are 150 rows of simulation cache logs with cache_gb and simulation_seconds — flag any IQR outliers in the cache_gb field for me.”
Imputing missing simulation seconds
“My simulation dataset has some null simulation_seconds values — can you fill them in using mean imputation across these records?”
Full data quality check on cache records
“Run both IQR outlier flagging and missing-value fill on this simulation cache dataset — use median to fill missing cache_gb values across these 80 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-180?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have up to 200 rows of simulation cache records and need both IQR outlier detection and missing-value imputation in a single low-cost call ($0.01 USDC). Ideal for lightweight, offline data quality checks on simulation cache metrics (cache_gb, simulation_seconds) without requiring an external data science environment or live prediction service.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, cache_gb, simulation_seconds, asset_group) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Unsupported method enum value returns validation error
- All values present with no nulls results in empty missing-value fill report
- All values within IQR results in no outlier flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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