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Feature Availability — Record Quality Audit + Numeric Distribution
x402
Feature Availability — Record Quality Audit + Numeric Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs two local analytical reports (record quality audit and numeric distribution summary) on up to 200 feature availability records containing populated_features and expected_features fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution 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 analytical reports: (1) a record quality audit flagging issues such as null values, out-of-range values, or malformed entries across the supplied rows, and (2) a numeric distribution summary including quantiles, min, max, mean, and spread statistics for the selected field (populated_features or expected_features), covering up to 200 rows processed entirely with local analytical engines.
Example use cases
ML feature store completeness check
“I have 150 rows of feature availability records from my ML pipeline with populated_features and expected_features per date and dataset group — can you run the quality audit and distribution report on populated_features to see how complete my feature store is?”
Daily dataset group comparison audit
“Run a record quality audit and numeric distribution on these 80 feature availability records across group-a and group-b for the past month, focusing on the expected_features field so I can see how our target feature counts are distributed.”
Data pipeline health monitoring
“Here are 200 rows of feature availability records from our data pipeline — each has a row_id, date, dataset_group, populated_features, and expected_features. Run the quality audit and distribution summary on populated_features so I can spot any anomalies before our weekly review.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-004?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate and understand the numeric distribution of feature availability data (populated vs expected features) for up to 200 rows in a single low-cost call. It is ideal for data quality pipelines, ML feature store audits, and dataset completeness checks where you want both a quality audit and a distribution summary together without any external lookups or live data dependencies. Prefer it over general-purpose analytics when your data fits the specific feature availability schema (populated_features, expected_features, dataset_group, date).
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, populated_features, expected_features) causes rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is not accepted (minItems: 1)
- Invalid field enum value other than 'populated_features' or 'expected_features' causes error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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