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Record Routing Analytics: Adjacent Row Changes + Secondary-Measure Weighted Mean
x402
Record Routing Analytics: Adjacent Row Changes + 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 2026-10-23).
Computes adjacent row-to-row changes and a secondary-measure weighted mean for up to 200 record routing rows (accepted_records, rejected_records) in two bundled analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied record routing records (accepted_records, rejected_records). 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) adjacent row changes showing the difference in the chosen measure between each consecutive row, and (2) a secondary-measure weighted mean summarizing the dataset. Results are computed locally from supplied data with no external lookups.
Example use cases
Daily pipeline acceptance rate audit
“I have a week of record routing logs with accepted and rejected counts per day. Can you calculate how accepted_records changed between each consecutive row and give me the weighted mean of rejected_records weighted by accepted_records? Here are the rows: row-1 2026-11-01 group-a 500 accepted 20 rejected, row-2 2026-11-02 group-a 480 accepted 35 rejected, row-3 2026-11-03 group-a 510 accepted 18 rejected.”
Multi-group routing quality comparison
“Run the adjacent row changes and secondary-measure weighted mean analysis on these record routing entries across two groups — row-1 2026-12-01 group-alpha 1000 accepted 50 rejected, row-2 2026-12-01 group-beta 800 accepted 120 rejected, row-3 2026-12-02 group-alpha 1050 accepted 45 rejected, row-4 2026-12-02 group-beta 750 accepted 130 rejected. Use rejected_records as the primary field.”
ETL batch rejection spike detection
“I need to spot where rejected_records spiked between consecutive batches. Please compute the adjacent row changes and the weighted mean for rejected_records (weighted by accepted_records) across these rows: row-1 2027-01-10 batch-1 2000 accepted 10 rejected, row-2 2027-01-11 batch-1 1950 accepted 80 rejected, row-3 2027-01-12 batch-1 2100 accepted 15 rejected, row-4 2027-01-13 batch-1 2050 accepted 12 rejected.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular record routing data (accepted/rejected counts) and need two specific statistical outputs — adjacent row deltas and a weighted mean — computed offline from your own supplied data without any external data fetch, prediction, or live lookup. Ideal for ETL pipeline quality monitoring, batch reporting, and data acceptance trend analysis on datasets up to 200 rows.
Side effects
- More than 200 rows supplied — request will be rejected or truncated
- Missing required fields (row_id, date, accepted_records, rejected_records) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Null values for both measures — weighted mean may be undefined or return error
- Field enum value not matching accepted_records or rejected_records — invalid input error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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