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Feature Availability Analytics: Adjacent Row Changes + Date-by-Category Pivot
x402
Feature Availability Analytics: Adjacent Row Changes + Date-by-Category Pivot 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 analytical reports on feature availability records — adjacent row change detection and a date-by-category pivot — for up to 200 rows of populated vs. expected feature data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot 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) an adjacent row changes report showing delta values between consecutive rows for the selected field (populated_features or expected_features), and (2) a date-by-category pivot table summarizing the selected field broken down by dataset_group across dates — both computed locally from the supplied data with no external lookups.
Example use cases
Feature coverage drift detection
“I have 150 rows of daily feature availability records with populated_features and expected_features for three dataset groups. Can you run the adjacent row changes report on populated_features so I can see where coverage drops or spikes between consecutive rows?”
Date-by-group pivot for completeness review
“Run the date-by-category pivot on my feature availability dataset — I want to see how populated_features vary across my dataset groups (group-alpha, group-beta, group-gamma) for each date from 2026-09-01 to 2026-09-30, up to 120 rows total.”
Expected vs populated feature audit
“Can you analyze my feature availability records using both the adjacent row change detection and the date-category pivot? I want to understand where expected_features and populated_features diverge across our dataset groups over the last quarter — I'll supply about 80 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-160?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two specific analytical transformations — adjacent row change detection and date-by-category pivoting — applied simultaneously to feature availability records (populated vs. expected features) in a single low-cost call. It is best suited for data quality monitoring, ETL pipeline auditing, and feature store completeness tracking where you already have the data and need structured analytical summaries without any live lookup or external data enrichment.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- field enum value not one of 'populated_features' or 'expected_features' — rejected
- Null values in numeric fields may affect change calculations — partial results or NaN deltas
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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