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Dataset Storage Analytics: Adjacent Row Changes + IQR Outlier Flags
x402
Dataset Storage Analytics: Adjacent Row Changes + 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 2026-10-19).
Analyzes up to 200 dataset storage records to produce two reports: adjacent row-to-row changes and IQR-based outlier flags for used_mb or row_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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 for the supplied dataset storage rows: (1) an adjacent row change report showing delta values between consecutive rows for the selected field, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. No external data is fetched; all analysis is performed locally on the supplied records.
Example use cases
Storage spike detection in daily snapshots
“I have daily storage snapshots for my datasets from the past month — can you check the used_mb field for IQR outliers and flag any days where storage jumped dramatically compared to the previous row?”
Row count anomaly audit across dataset groups
“Run an adjacent row change analysis and IQR outlier check on the row_count field for these 150 records across my dataset groups — I want to know which rows look statistically abnormal.”
Pre-migration data quality check
“Before I migrate these dataset records, can you flag any outliers and unusual jumps in used_mb using IQR analysis and adjacent row comparisons? I have about 80 records with dates and group labels.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local statistical analysis of dataset storage metrics (used_mb or row_count) without any external lookups or predictions. It is ideal for data quality checks, anomaly pre-screening, and storage trend monitoring on batches of up to 200 rows. Prefer it over general-purpose analytics platforms when you want a fast, low-cost ($0.01 USDC for both reports) offline computation against your own supplied data.
Side effects
- Exceeds 200 row limit — request rejected
- Missing required fields (row_id, date, used_mb, row_count, dataset_group) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema rejection
- Invalid field enum value (must be 'used_mb' or 'row_count') — error
- Numeric values out of allowed range — validation failure
- Empty rows array — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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