triage-tickets

name	triage-tickets
description	Triage support tickets or issues — categorise, assign severity, identify routing, and flag patterns.
argument-hint	[ticket content, issue list, or path to issues]
user-invocable	true
allowed-tools	Read, Bash, Glob, Grep

Triage $ARGUMENTS using the mandatory process below. Every step is required — do not skip any.

Step 1 — Ingest and normalise

Read every ticket. For each, extract:

• Ticket ID (or assign a sequential one if absent)
• Reporter (name, account, plan tier if available)
• Raw summary — one sentence capturing what the user actually said
• Reproduction signal — does the ticket contain steps to reproduce, error messages, or screenshots?

If tickets are in a file or directory, use Read, Glob, or Grep to pull them. If they are inline, parse directly.

Step 2 — Classify each ticket

Apply ALL of the following dimensions to every ticket:

Category

Category	Use when
Bug	Something that worked before is now broken, or behaves contrary to documentation
Feature request	User wants new functionality that does not exist
How-to question	User is trying to accomplish something and needs guidance
Account / billing	Login, permissions, invoices, plan changes
Integration issue	Problem occurs at the boundary with a third-party system
Performance	Slowness, timeouts, resource exhaustion — system works but poorly
Data issue	Missing, corrupted, or incorrect data
Complaint	Expressing dissatisfaction without a specific technical issue

Severity

Severity	Definition	Response target	Examples
Critical	System down, data loss, security breach, or complete blocker for multiple users	Acknowledge within 1 hour, update every 2 hours	Production outage, data deletion, auth bypass
High	Major feature broken for a user, no workaround available	Acknowledge within 4 hours, update daily	Cannot export data, payment processing fails
Medium	Feature degraded but workaround exists, or issue affects a non-critical path	Acknowledge within 1 business day	Slow report generation, UI glitch on one browser
Low	Cosmetic issue, minor inconvenience, or question with self-serve answer	Acknowledge within 2 business days	Typo in UI, color preference, "how do I?"

Severity escalation rules:

• Enterprise or high-tier customer reporting → raise severity by one level
• Issue mentions data loss or security → automatically Critical
• Issue blocks a user from core workflow → at least High

Routing

Route to	When
Engineering — bugs	Confirmed bug with reproduction steps
Engineering — infrastructure	Performance, uptime, scaling issues
Product	Feature requests, UX complaints with 3+ occurrences
Documentation	How-to questions answerable by better docs
Support (self)	Account, billing, known-issue with documented workaround
Security	Any mention of unauthorised access, data exposure, vulnerability

Workaround

For every ticket, answer: Is there a known workaround?

• If yes: state the workaround in 1-2 sentences, cite the source (KB article, prior ticket)
• If no: state "No known workaround" explicitly
• If uncertain: state "Workaround unknown — needs investigation"

Step 3 — Detect patterns

After classifying all tickets, scan for patterns:

• Duplicate cluster: 2+ tickets describing the same root issue → group them, note the count
• Escalation trigger: 3+ tickets on the same issue → flag for immediate escalation to product/engineering with this template:

PATTERN ESCALATION
Issue: [one-line description]
Ticket count: [N]
Affected users: [list or count]
Severity: [highest severity among the cluster]
Sample ticket IDs: [up to 5]
First reported: [date of earliest ticket]
Workaround available: [yes/no]
Recommended action: [suggested next step]

• Regression signal: issue appeared after a known deploy or release → flag with the release version
• Trend direction: is this issue increasing, stable, or decreasing over the batch?

Step 4 — Generate bug reports for engineering

For every ticket routed to engineering, produce a structured bug report:

## Bug Report: [title]

**Source tickets**: [IDs]
**Severity**: [level]
**Category**: [bug / performance / infrastructure]

### Description
[What is happening, in plain language]

### Steps to reproduce
1. [Step]
2. [Step]
3. [Step]

### Expected behaviour
[What should happen]

### Actual behaviour
[What happens instead]

### Environment
- Product version: [if known]
- Browser/OS/client: [if known]
- Account/plan: [if known]

### Supporting evidence
[Error messages, screenshots, logs — quote directly from the ticket]

### Workaround
[If any]

If the ticket lacks reproduction steps, state what is missing and recommend the support agent ask the user for specific information.

Step 5 — Output

Triage table

Present ALL tickets in a single table, sorted by severity (Critical first), then by category:

Ticket ID	Summary	Category	Severity	Route to	Workaround	Pattern cluster
...	...	...	...	...	...	...

Pattern summary

After the table, list:

1. Escalations triggered — any pattern clusters hitting the 3+ threshold
2. Regression candidates — issues potentially linked to recent changes
3. Workaround gaps — high/critical tickets with no known workaround (these need KB articles)

Metrics

• Total tickets triaged: [N]
• By severity: Critical [N], High [N], Medium [N], Low [N]
• By routing: Engineering [N], Product [N], Docs [N], Support [N], Security [N]
• Patterns detected: [N] clusters covering [N] tickets

Rules

• Never downplay a user's reported severity without evidence. If a user says "this is urgent," treat it as at least High until investigation says otherwise.
• When in doubt about category, pick the one that gets the ticket to the right team faster.
• Always preserve the user's original language in the summary — do not sanitise or corporate-speak their words.
• If a ticket contains multiple issues, split it into separate entries in the triage table.
• Every Critical and High ticket MUST have a recommended next action, not just a routing destination.

Related Skills

• /support:feedback-synthesis — after triaging a batch, synthesise recurring themes into product feedback.
• /support:write-kb-article — when triage reveals a common user issue, write a KB article to reduce future ticket volume.