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Join Coverage Analytics: Adjacent Row Changes + Date-by-Category Pivot
x402
Join Coverage 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 2026-10-15).
Analyzes join coverage data (matched_rows, source_rows) across up to 200 rows, producing two reports: adjacent row-over-row change analysis and a date-by-category pivot table.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied join coverage records (matched_rows, source_rows). 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 computed locally on the supplied data: (1) an adjacent row changes report showing how matched_rows or source_rows shift between consecutive records, and (2) a date-by-category pivot table summarizing the chosen metric across dataset groups and dates. No external lookups or predictions are made; all analysis is based solely on the input rows provided (up to 200 rows).
Example use cases
ETL pipeline join coverage audit
“I have daily join coverage stats for my ETL pipeline across two dataset groups — 'raw_events' and 'enriched_events' — from 2026-09-01 to 2026-09-30, with matched_rows and source_rows for each day. Can you run the adjacent row changes analysis and the date-by-category pivot on matched_rows so I can spot where coverage dropped?”
Data quality monitoring by group
“I want to monitor source_rows across my dataset groups 'customers' and 'orders' over the past two weeks. Can you give me the row-over-row changes and a pivot table by date and group for the source_rows field?”
Ad-hoc join coverage comparison across categories
“Here are 50 records of join coverage data across four dataset groups — 'pageviews', 'sessions', 'conversions', and 'signups' — each with a date, matched_rows, and source_rows. Run the adjacent row changes and the date-by-category pivot for matched_rows so I can compare how each group tracks over time.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-160?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected join coverage tabular data (matched_rows and source_rows per date and dataset group) and need two specific analytical views — row-over-row change detection and a date-by-category pivot — without any external data lookup, prediction, or live monitoring. It is ideal for ad-hoc, offline analysis of up to 200 records at a very low cost ($0.01 USDC for both reports). Prefer alternatives if you need more than 200 rows, live data ingestion, or different analytical transformations such as time-series forecasting or anomaly detection.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null values in numeric fields may affect adjacent row change calculations
- field enum value not one of 'matched_rows' or 'source_rows' — validation error
- Empty rows array — no analysis possible
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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