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Feature Availability Analytics — String Normalisation + Secondary-Measure Weighted Mean
x402
Feature Availability Analytics — String Normalisation + 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 2026-10-15).
Computes string normalisation and secondary-measure weighted mean statistics over feature availability records (populated_features, expected_features) for up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — String normalisation + Secondary-measure weighted mean: String normalisation + 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
Two independent analytical reports per call: (1) a string normalisation report and (2) a secondary-measure weighted mean report, both computed locally over the supplied feature availability rows (up to 200). No external lookups or predictions are included.
Example use cases
Feature completeness audit by dataset group
“I have a batch of 50 feature availability records across three dataset groups with dates ranging through Q4 2025. Can you run string normalisation and weighted mean analytics on the populated_features field so I can see which groups have the best feature coverage?”
Data pipeline quality reporting
“Our pipeline tracks populated_features and expected_features for each daily batch run — I've got about 120 rows from the last four months. Run string normalisation plus secondary-measure weighted mean on the expected_features field so I can generate a quality report.”
Comparing feature fill rates across experiment groups
“I'm comparing two experiment groups on feature availability — group-control and group-variant — with about 80 date-stamped rows total. Can you apply string normalisation and secondary-measure weighted mean to the populated_features column and give me the two analytical reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local statistical analysis of feature availability data — specifically string normalisation and secondary-measure weighted mean — over up to 200 rows, without requiring external lookups, live data, or professional assessments. It delivers two bundled reports per call at a fixed low cost, making it ideal for routine data quality checks or pipeline monitoring tasks.
Side effects
- Exceeding 200 rows causes a validation error
- Missing required fields (row_id, date, populated_features, expected_features) cause rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values for both populated_features and expected_features may produce undefined weighted mean results
- Invalid field enum value (not 'populated_features' or 'expected_features') causes parameter rejection
- Payment failure via x402 protocol blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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