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Feature Availability — Record Quality Audit + Trailing Mean Series
x402
Feature Availability — Record Quality Audit + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Runs two local analytical reports on supplied feature availability data: a record quality audit and a trailing mean series, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Record quality audit + Trailing mean series: Record quality audit + Trailing mean series 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 highlighting issues, completeness rates, or anomalies in the supplied feature availability rows, and (2) a trailing mean series showing rolling averages of the selected field (populated_features or expected_features) over the specified window — all computed locally without any live lookup or external data.
Example use cases
ML pipeline feature completeness check
“I need to audit the quality of my feature availability records for last month — I have 50 rows across three dataset groups with populated_features and expected_features counts per day. Can you run a quality audit and also give me a trailing mean series with a window of 7 on the populated_features field?”
Rolling average for feature monitoring dashboard
“Compute a trailing mean series with a window of 5 for the expected_features column across my 30-row feature availability dataset spanning October 2026 — I want to see how the rolling average trends over time across my dataset groups.”
Spotting data gaps in training dataset
“I want to check if there are quality issues in my training dataset's feature availability records — I've got 100 rows with row IDs, dates, dataset groups, and both populated and expected feature counts. Run a record quality audit and a trailing mean on populated_features with a window of 10.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-010?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to assess the quality of feature availability records AND compute rolling averages in a single low-cost call. It is ideal for ML/data engineering workflows where you want to validate feature population completeness and monitor trends over time, without requiring a live data connection or external lookup. Best suited for batch sizes of up to 200 rows where two complementary reports (quality audit + trailing mean) are both needed at once.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Date strings not in YYYY-MM-DD format — pattern validation failure
- Window value outside range 2–200 — rejected
- Null values in numeric fields may affect mean calculation output
- Invalid field enum value — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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