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Product Feature Telemetry – IQR Outlier Flags
x402
Product Feature Telemetry – 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 2026-10-21).
Detects IQR-based outliers in product feature telemetry rows (events or unique_users) and returns a bounded outlier flags report for up to 200 records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product feature telemetry - IQR outlier flags: IQR outlier flags for Product feature telemetry. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.
Output
A bounded report listing which of the supplied telemetry rows are flagged as IQR outliers for the chosen field (events or unique_users), based on local descriptive statistics computed over the submitted batch — no external enrichment or forecasting.
Example use cases
Spike detection in feature rollout
“I just ran a feature rollout and have 50 rows of daily telemetry with event counts per feature group. Can you flag any IQR outliers in the events field so I know which days had abnormal spikes?”
Unusual user activity by feature
“Here's 30 rows of product telemetry — each row has a feature name, date, and unique_users count. Flag any rows where unique_users is an IQR outlier so I can investigate anomalies before my report.”
QA check on ingested telemetry batch
“Before I archive this batch of 120 feature telemetry records, can you run an IQR outlier check on the events column and tell me which row_ids are flagged as outliers?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-feature-telemetry-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local IQR-based outlier detection on a batch of up to 200 product feature telemetry records without requiring external data enrichment or forecasting. It is ideal for QA pipelines, pre-archive checks, and quick anomaly flagging on events or unique_users metrics grouped by feature and date.
Side effects
- Empty or missing rows array returns a validation error
- row_id missing or exceeds 80 characters causes schema rejection
- date field not matching YYYY-MM-DD pattern fails validation
- field enum value not 'events' or 'unique_users' returns error
- Submitting more than 200 rows exceeds maxItems limit
- Numeric values outside ±1,000,000,000,000 fail schema bounds
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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