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Feature Availability Analytics — Base-100 Series & Secondary-Measure Weighted Mean
x402
Feature Availability Analytics — Base-100 Series & Secondary-Measure Weighted Mean 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).
Computes a Base-100 index series and a secondary-measure weighted mean for feature availability records (populated_features vs expected_features) over up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Base-100 series + Secondary-measure weighted mean: Base-100 series + Secondary-measure weighted mean 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 for the supplied feature availability rows: (1) a Base-100 index series normalizing the chosen field (populated_features or expected_features) relative to the selected base row, and (2) a secondary-measure weighted mean across all supplied rows. Both reports are computed locally on the submitted data with no external lookups.
Example use cases
Daily feature completeness indexing
“I've got a week of data quality records — 7 rows, each with a date, a dataset_group label, and counts for populated_features and expected_features. Can you index the populated_features column to base-100 starting from the first row and also give me the weighted mean across all rows?”
Cross-group feature coverage comparison
“I have 20 rows of feature availability records split across three dataset groups (group-alpha, group-beta, group-gamma) with daily dates and both populated and expected feature counts. Run both the Base-100 series and the secondary weighted mean on expected_features with base index 0 so I can compare coverage across groups.”
Data pipeline audit for populated fields
“We ran a pipeline audit and collected 50 rows of feature availability stats per day — each row has a row_id, date, dataset_group, populated_features, and expected_features. Generate the Base-100 index series and weighted mean on populated_features starting from index 0, so we can spot where feature population dropped below baseline.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-166?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of feature availability records (populated vs expected feature counts) and need both a normalized Base-100 time series and a weighted mean in a single low-cost call ($0.01 USDC). Ideal for data quality audits, pipeline monitoring, and dataset completeness reporting where no external data lookup or prediction is needed — pure local analytical computation on your supplied data.
Side effects
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation failure
- base_index out of range (>=number of rows) may cause indexing errors
- Null values in populated_features or expected_features may affect weighted mean computation
- Malformed JSON body returns a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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