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Feature Availability Analytics: Date-by-Category Pivot + Keyed Dataset Comparison
x402
Feature Availability Analytics: Date-by-Category Pivot + Keyed Dataset Comparison 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).
Runs two analytical reports on feature availability data: a date-by-category pivot table and a keyed comparison between two datasets, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Date by category pivot + Keyed dataset comparison: Date by category pivot + Keyed dataset comparison 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 analytical reports: (1) a date-by-category pivot table aggregating the chosen field (populated_features or expected_features) across dataset groups and dates; (2) a keyed dataset comparison between the primary and secondary supplied datasets, highlighting differences by row key. Results are computed locally from supplied data with no external lookups.
Example use cases
ML feature store completeness audit
“I have two batches of feature availability records from our ML feature store — each with row IDs, dataset groups, dates, and counts of populated vs expected features. Can you pivot the primary batch by date and category, and then compare it against the secondary batch to spot discrepancies?”
Daily feature pipeline monitoring
“Here are today's feature availability logs across our three dataset groups for the past 30 days, up to 150 rows. Please pivot them by date and group using the expected_features field, and compare this week's data against last week's to see what changed.”
Cross-team dataset feature gap analysis
“We have feature records from two different teams — each row has a date, a group label, and counts of how many features were populated vs expected. Can you run a pivot table by date and category on Team A's data, and then do a keyed comparison against Team B's data to find where they diverge?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-188?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a date-by-category pivot and a keyed cross-dataset comparison on feature availability data in a single low-cost call. Ideal for ML pipeline quality monitoring, feature store audits, and comparing feature completeness between two dataset versions — all without any external data lookups or predictions. Best suited for structured tabular data up to 200 rows with clearly keyed rows and group labels.
Side effects
- Exceeds 200-row limit — request rejected or truncated
- Missing required fields (row_id, date, dataset_group) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Null values in numeric fields may affect aggregation accuracy
- Mismatched row_ids between primary and secondary datasets reduce comparison coverage
- Empty rows array — no output generated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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