Skill: Customer Support Triage
A reusable support triage skill for classifying requests, assessing urgency, gathering missing context, and preparing a safe first reply.
Use cases
Customer Support, Operations & Workflow
Platforms
Claude, GPT, Model-Agnostic
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The resource
Copy and adapt. Do not paste blind.
# Customer Support Triage Skill
Core rules:
- First classify the issue type, urgency, and likely owner.
- Separate what the user actually asked from what the system assumes.
- If critical context is missing, request it before offering a confident solution.
- Escalate bugs, billing risks, security concerns, and account-access issues clearly.
- Do not answer policy questions from memory if the policy text is not present.
- Keep tone calm, but never let tone override severity.
Default output:
1. Issue category
2. Priority level
3. Required information still missing
4. Recommended next action: reply | clarify | escalate
5. Draft reply
6. Internal escalation note
Never invent policy, refunds, security guarantees, or product behaviour.When to Use This
Use this when you want faster first-pass handling of incoming support requests without sacrificing safety, escalation discipline, or trust.
It is useful for small support teams, founders doing support themselves, and ops teams trying to standardise what gets routed where before a human gives the final answer.
Why It Works
The skill works because it treats triage and response as different jobs. First classify the request. Then decide whether the system should reply, ask for clarification, or escalate.
The “never invent policy” rule is the critical guardrail. Support systems become dangerous when they sound authoritative about things they do not actually know.
How to Customise
Add your own queue logic, priority labels, escalation owners, and risk categories if your team already has them.
If your product has strict policy boundaries, add explicit refund, account, compliance, and security rules into the skill.
Limitations
This is a triage layer, not a full support knowledge base. It still needs accurate policy and product context around it.
Sensitive requests, angry customers, and anything with legal, billing, or security consequences still deserve human review.
Model Notes
Claude is strong at separating tone from severity and staying measured.
GPT is useful if you need the output in a more rigid queue-friendly format. Model-agnostic overall as long as the escalation rules are explicit.
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