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Dataset Storage Analytics — Numeric Distribution & IQR Outlier Flags
x402
Dataset Storage Analytics — Numeric Distribution & 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 2026-10-15).
Computes numeric distribution statistics and IQR-based outlier flags for used_mb and row_count fields in dataset storage records (up to 200 rows, 2 reports per call).
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied dataset storage records (used_mb, row_count). 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 (one per selected field or both used_mb and row_count): numeric distribution statistics (min, max, mean, median, quartiles) and IQR-based outlier flags identifying which rows fall outside the expected range. Each report covers up to 200 input rows with no external data lookups.
Example use cases
Storage anomaly detection in ETL pipeline
“I have 150 daily dataset storage records with used_mb and row_count values — can you run IQR outlier detection on used_mb to find which days had abnormally large storage usage?”
Distribution audit before data migration
“Before we migrate our datasets, can you compute the numeric distribution statistics for row_count across all our storage records so we know the spread, quartiles, and any outliers?”
Ongoing storage health check for multiple groups
“We track storage under two dataset groups, 'warehouse' and 'archive'. Can you flag any IQR outliers in used_mb across both groups using our last 90 days of records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-080?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need fast, local statistical analysis (distribution + IQR outlier detection) on dataset storage records (used_mb, row_count) without any external lookups or predictions. Ideal for data quality audits, anomaly detection in storage metrics, and pre-migration dataset reviews. Best for batch sizes up to 200 rows where two analytical reports are needed affordably at $0.01 USDC per call.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (±1 trillion) — rejected
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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