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Feature Availability Record Quality Audit + IQR Outlier Flags
x402
Feature Availability Record Quality Audit + 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).
Runs a record quality audit and IQR-based outlier detection on up to 200 feature availability records (populated_features, expected_features), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied feature availability records (populated_features, expected_features). 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 independent reports: (1) a record quality audit identifying missing, malformed, or invalid rows among the supplied feature availability records, and (2) an IQR-based outlier detection report flagging rows where the selected field (populated_features or expected_features) falls outside the interquartile range bounds. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
ML pipeline feature completeness audit
“I've got 120 rows of feature availability data from my ML pipeline — each row has a date, a dataset group, and counts of populated vs expected features. Can you run a record quality audit on them and flag any outliers in populated_features using IQR?”
Data health check before model training
“Before we kick off model training, can you audit these feature availability records for quality issues and identify any rows where expected_features looks like an outlier? I have about 80 rows across three dataset groups from last quarter.”
Anomaly detection in feature tracking logs
“We track feature population rates daily across several dataset groups — can you analyze these 150 rows of feature availability logs, run the record quality audit, and flag any IQR outliers in the expected_features field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and IQR outlier detection on feature availability records in a single low-cost call ($0.01 USDC). Ideal for ML/data engineering workflows where you want to validate feature completeness data before model training or pipeline steps, without requiring any external data sources or live predictions.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Null numeric values may affect IQR computation if too prevalent
- Payment of 0.01 USDC not completed results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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