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Ticket Escalations – Adjacent Row Changes
x402
Ticket Escalations – Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Computes adjacent row-to-row changes for ticket and escalation count data across teams and dates, returning a local descriptive analytics report.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket escalations - Adjacent row changes: Adjacent row changes for Ticket escalations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded adjacent row changes report.
Output
Returns a bounded adjacent row changes report containing the computed delta between each consecutive pair of supplied records for the chosen field (tickets or escalations), expressed as local descriptive analytics without forecasting or external enrichment.
Example use cases
Daily escalation trend monitoring
“I have daily ticket and escalation counts for my support teams over the past two weeks — can you compute the row-by-row changes in escalation numbers so I can see which days had the biggest jumps?”
Multi-team ticket volume comparison
“Here are 50 records across four support teams with daily ticket counts — run an adjacent row changes report on the tickets field so I can see how each team's volume shifted day over day.”
Incident spike detection in escalations
“I've got a batch of 80 support records for my escalations field from last month — can you calculate the adjacent row deltas so I can spot any sudden spikes or drops between consecutive entries?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-ticket-escalations-period-change?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have up to 200 sequential ticket or escalation records and need to understand period-to-period (row-to-row) changes without requiring forecasting, enrichment, or external data. It is ideal for local descriptive analytics on support team data where the goal is spotting consecutive shifts or deltas in ticket or escalation volumes.
Side effects
- Missing required fields (row_id, team, date, tickets, escalations) causes validation error
- Supplying more than 200 rows exceeds maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null values in the target field may produce null deltas in output
- Invalid field enum value (anything other than 'tickets' or 'escalations') causes rejection
- Empty rows array (fewer than 1 item) fails minItems constraint
- Payment of 0.01 USDC not provided or rejected causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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