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Feature Availability Analytics — Category Frequencies + Trailing Mean Series
x402
Feature Availability Analytics — Category Frequencies + 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 2026-10-19).
Analyzes feature availability records to produce two reports: category frequency distributions and trailing mean time series, for up to 200 rows of populated/expected feature data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Category frequencies + Trailing mean series: Category frequencies + 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 analytical reports derived from the supplied rows: (1) a category frequency distribution showing how often each dataset_group appears and/or how feature values distribute across categories, and (2) a trailing mean time series computed over the specified window for the selected field (populated_features or expected_features). No external lookups or predictions are made — all computation is local to the supplied data.
Example use cases
Feature coverage audit by dataset group
“I have 150 rows of feature availability records across 5 dataset groups with daily dates and populated vs expected feature counts — can you run a category frequency breakdown and a trailing mean series with a window of 7 on the populated_features field so I can see which groups are consistently underperforming?”
Rolling average for data pipeline health
“Run a trailing mean series with a window of 5 on my expected_features column across these 80 daily pipeline records — I want to see how the smoothed trend compares to my raw values and also get the category frequency breakdown by dataset_group.”
QA report for ML feature store
“I need two quick reports on my feature store QA data: category frequencies by dataset group and a 14-day trailing mean for populated_features. I have 200 rows with row IDs, dates, and both populated and expected feature counts per record.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-103?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of feature availability records and need both a category frequency distribution and a trailing mean time series in a single low-cost call ($0.01 USDC for both reports). It is ideal for lightweight, offline analytical tasks on pre-collected data where no live data fetching, predictions, or external enrichment is required. Prefer alternatives if you need more than 200 rows, real-time data ingestion, or a different type of statistical analysis (e.g. outlier detection, elasticity, period-over-period comparison).
Side effects
- Fewer than 1 row or more than 200 rows supplied — request rejected
- Window size larger than the number of rows — trailing mean may be undefined or reduced
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Malformed date strings not matching YYYY-MM-DD pattern — parse error
- Null values in numeric fields may affect trailing mean computation
- Invalid field enum value (not 'populated_features' or 'expected_features') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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