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Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication & IQR Outlier Flags
x402
Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication & 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 three local analytical reports on feature availability records: a record quality audit, exact row deduplication, and IQR-based outlier flagging — for up to 200 rows of populated_features / expected_features data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + IQR outlier flags: Record quality audit + Exact row deduplication + IQR outlier flags for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit highlighting malformed or problematic rows, (2) an exact row deduplication report identifying duplicate records, and (3) IQR-based outlier flags marking statistically anomalous values in the selected numeric field (populated_features or expected_features). All computed locally with no external lookups.
Example use cases
Pre-pipeline feature data validation
“Before I push this feature availability dataset into my ML pipeline, can you audit the data quality, remove exact duplicate rows, and flag any IQR outliers in the populated_features column? I have about 150 rows with row_id, dataset_group, date, populated_features, and expected_features.”
Detecting anomalies in feature completeness tracking
“I'm tracking how many features are populated versus expected across multiple dataset groups over the past few months. Can you run an outlier analysis using IQR on the expected_features field and also check for any duplicate records or data quality issues?”
Auditing feature store records for reporting
“I need a data integrity report on my feature availability records — specifically a quality audit, exact row deduplication, and IQR outlier flags on the populated_features values. I have around 80 rows across groups like group-a and group-b with dates in 2026.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-220?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations — quality audit, deduplication, and IQR outlier detection — on feature availability records in a single low-cost call. Ideal for validating feature store data before ML pipeline ingestion, or for periodic data health checks on datasets up to 200 rows. Prefer this over separate single-purpose analytics tools when cost efficiency and bundled reporting on populated_features / expected_features data is the priority.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Null values in both populated_features and expected_features may reduce outlier detection accuracy
- Fewer than 4-5 rows may produce unreliable IQR statistics
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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