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Bundle Contribution IQR Outlier Flags
x402
Bundle Contribution 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 2026-10-16).
Identifies IQR-based statistical outliers across a batch of retail bundle contribution records using supplied price and cost figures.
Price / call$0.01
Success—
StatusUnknown
How this service works
Bundle contribution - IQR outlier flags: IQR outlier flags for Bundle contribution. Supplied retail figures only. Local calculation and scenario analysis; no live inventory, account access, platform fee lookup or order execution. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded iqr outlier flags deliverable.
Output
Returns IQR outlier flags for each submitted bundle record, indicating which rows fall outside the interquartile range of calculated bundle contribution values, based entirely on the supplied figures with no external data lookups.
Example use cases
Detecting anomalous bundle margins
“I have 10 bundle SKUs with varying prices and costs — can you run IQR outlier detection on them to see which ones have abnormal contribution margins? Bundle prices range from 80 to 200, item costs from 40 to 90, packing costs around 5–8, and fulfilment costs around 10–15.”
Cleaning bundle data before reporting
“Before I build my quarterly bundle profitability report, can you flag any statistical outliers in this dataset of 20 bundle records using IQR analysis? I want to know which rows are unusual so I can review them before including them.”
Scenario analysis on promotional bundles
“I'm testing different bundle pricing scenarios — can you flag which of these 15 bundle configurations are outliers by IQR standards? Each has a bundle price, item costs, packing cost, and fulfilment cost.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-retail-bundle-contribution-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local IQR-based outlier detection on retail bundle contribution data using only your own supplied figures, without needing live inventory, platform fees, or order execution. It is ideal for data cleaning, anomaly detection in batch bundle datasets, or scenario analysis on bundle pricing and cost structures.
Side effects
- Fewer than 2 rows submitted — minimum row count not met
- Values exceed maximum allowed (1,000,000,000,000) causing validation error
- Missing required fields (bundle_price, item_costs, packing_cost, fulfilment_cost) in any row
- More than 200 rows submitted — exceeds maxItems limit
- All records have identical contribution values resulting in zero IQR spread
- Payment not included or insufficient — x402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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