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Feature Availability Analytics: Adjacent Row Changes + Two-Measure Correlation
x402
Feature Availability Analytics: Adjacent Row Changes + Two-Measure Correlation 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 adjacent row change analysis and two-measure correlation for feature availability data (populated_features vs expected_features) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation 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 for the supplied feature availability rows: (1) an adjacent row changes report showing how the selected measure shifts between consecutive rows, and (2) a two-measure correlation report quantifying the statistical relationship between populated_features and expected_features across all rows.
Example use cases
Daily feature fill rate monitoring
“I have daily records of how many features we expected vs actually populated across two dataset groups over the past week. Can you analyze the row-by-row changes in populated_features and also check how correlated populated_features is with expected_features across all those dates?”
Data pipeline quality audit
“Our data pipeline produces feature availability logs each day. I want to see where populated_features jumps or drops significantly between consecutive rows, and whether there's a strong correlation between what we expected and what actually got populated. Here's the dataset with row IDs, dates, groups, and both feature counts.”
Feature coverage regression check
“We just ran a batch of dataset processing jobs and I need a statistical check — specifically adjacent row changes and a two-measure correlation between populated_features and expected_features — to make sure nothing broke. I'll supply up to 50 rows with date, row_id, dataset_group, populated_features, and expected_features.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-158?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have feature availability records with both populated_features and expected_features columns and need both sequential change detection (adjacent row deltas) and correlation analysis in a single cheap call ($0.01 USDC). It is especially useful for data quality monitoring pipelines where you want to catch sudden drops in feature population rates and validate whether actual feature counts track expected counts over time, without needing an external analytics platform.
Side effects
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Submitting more than 200 rows exceeds the maxItems limit
- Null values in both measure fields for all rows may produce undefined correlation
- Payment failure (insufficient USDC balance) blocks processing
- Malformed JSON input returns a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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