AI Playbook
Recurring ticket investigation
Determine whether repeated tickets share an underlying user, device, service, or process cause.
Copy this prompt
Replace the bracketed values, then paste it into an AI client connected to Stackyapper.
Run the "Recurring ticket investigation" playbook using Stackyapper.
Inputs
- ticket id: [Current or representative ticket identifier]
- lookback: [Optional bounded history window] (optional)
Objective
Determine whether repeated tickets share an underlying user, device, service, or process cause.
Required evidence
- Service tickets
Use when available
- Ticket history
- Managed devices
- Device alerts
- User directory
- Documentation
Procedure
1. Load the current ticket and search for materially similar prior tickets.
2. Compare notes, configurations, time entries, schedules, and prior resolutions.
3. Check related device, alert, identity, and runbook evidence.
4. Identify the strongest recurring pattern and distinguish systemic cause from coincidence.
Return
- Recurrence pattern
- Prior resolutions
- Common factors
- Probable underlying cause
- Prevention recommendation
Use only evidence available through the Stackyapper Apps and permissions connected to this AI client. If required evidence is unavailable, say what is missing before continuing. Do not guess or make changes in connected systems.Before you paste
Replace every bracketed value in the prompt. Delete an optional input line if it does not apply.
ticket id: Current or representative ticket identifier. (required)lookback: Optional bounded history window. (optional)
What Stackyapper will use
The exact tools depend on the Apps connected to your workspace and the current user's permissions.
- Service tickets (required)
- Ticket history (used when available)
- Managed devices (used when available)
- Device alerts (used when available)
- User directory (used when available)
- Documentation (used when available)
What you'll get
- Recurrence pattern
- Prior resolutions
- Common factors
- Probable underlying cause
- Prevention recommendation
How it works
- Load the current ticket and search for materially similar prior tickets.
- Compare notes, configurations, time entries, schedules, and prior resolutions.
- Check related device, alert, identity, and runbook evidence.
- Identify the strongest recurring pattern and distinguish systemic cause from coincidence.