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Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication & Equal-Width Histogram
x402
Feature Availability Analytics — Record Quality Audit, Exact Row Deduplication & Equal-Width Histogram 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 independent analytical reports (record quality audit, exact row deduplication, and equal-width histogram) on supplied feature availability records containing populated_features and expected_features fields, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Exact row deduplication + Equal-width histogram: Record quality audit + Exact row deduplication + Equal-width histogram 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
Returns three independent analytical reports: (1) a record quality audit highlighting null values, format issues, and completeness metrics; (2) an exact row deduplication report listing identical rows; and (3) an equal-width histogram of the specified numeric field (populated_features or expected_features) partitioned into up to 30 bins. All computed locally without external lookups or predictions.
Example use cases
Feature completeness audit for ML pipeline
“I have 150 rows of feature availability records from our ML pipeline — each has a row_id, dataset_group, date, populated_features, and expected_features. Can you run a full quality audit, check for exact duplicate rows, and give me a 5-bin histogram on the expected_features field?”
Deduplication check on daily feature snapshot
“We captured a daily feature availability snapshot and I'm worried there are duplicate entries. Run the exact row deduplication report plus a quality audit on these records — there are about 80 rows with populated_features and expected_features columns.”
Distribution analysis of populated features
“Can you produce an equal-width histogram with 15 bins for the populated_features values across my feature availability dataset? Also include the record quality audit and dedup report while you're at it — I've got around 200 rows split across several dataset groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-219?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit data quality, remove exact duplicate rows, and understand the numeric distribution of feature availability records in a single paid call. It is ideal for ML feature store monitoring, dataset hygiene checks before model training, or periodic data pipeline validation — especially when you want three complementary analytical views for a flat $0.01 fee without any external API calls or live data lookups.
Side effects
- More than 200 rows submitted — request rejected, reduce row count
- Missing required fields (row_id, date, populated_features, expected_features) — validation error returned
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- bins value outside 1–30 range — rejected with parameter error
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
- Invalid field enum value (not populated_features or expected_features) — error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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