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ticket-triage
Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations.
Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports.
Write clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions.
Design structured customer onboarding workflows with phased checklists, email templates, success milestones, and ownership assignments.
Create and manage budgets with variance analysis and departmental allocation
Classify expenses by category, department, and tax deductibility from transaction data
| name | ticket-triage |
| description | Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. |
| license | MIT |
| metadata | {"author":"community","version":"1.0"} |
Automatically classify, prioritize, and route incoming customer support tickets to the right team with a suggested first response. This skill processes raw ticket text, identifies the customer's intent and key entities (product area, account tier, error codes), assigns a category and priority level, then routes to the appropriate team while drafting an empathetic initial reply.
Receive and parse the ticket — Ingest the raw ticket including subject, body, customer metadata (account tier, plan, tenure), and any attachments or screenshots. Normalize the text by stripping signatures, quoted replies, and boilerplate. Extract structured fields like order IDs, error codes, and URLs.
Extract intent and entities — Analyze the cleaned ticket text to determine the customer's primary intent (reporting a bug, asking a question, requesting a feature, disputing a charge). Identify named entities such as product names, feature areas, API endpoints, and specific error messages. Tag sentiment as positive, neutral, negative, or urgent.
Classify into category — Map the extracted intent to one of the standard categories: billing (charges, invoices, refunds, plan changes), bug (errors, crashes, unexpected behavior), feature-request (new functionality, integrations, enhancements), or how-to (setup, configuration, usage questions). If a ticket spans multiple categories, assign the primary and note secondaries.
Assign priority using impact and urgency — Score priority from P0 to P3 using a matrix. P0: production outage or data loss affecting multiple customers. P1: major functionality broken for a single enterprise account. P2: degraded experience with a workaround available. P3: general questions or minor cosmetic issues. Factor in account tier — enterprise accounts get a one-level priority bump.
Route to the appropriate team — Map the category and priority to a team: billing tickets go to the Finance Support queue, bugs route to Engineering Triage (P0/P1) or Product Support (P2/P3), feature requests go to Product Management, and how-to tickets go to Customer Education. Attach relevant context and internal notes.
Generate initial response — Draft a first reply that acknowledges the issue, sets expectations on response time based on priority, includes any immediate self-serve resources (KB articles, status page links), and asks clarifying questions if the ticket is ambiguous. Match tone to sentiment — empathetic for frustrated customers, concise for technical users.
Provide a support ticket (or batch of tickets) and ask the agent to triage them. Include customer metadata when available for better priority assignment.
Triage these support tickets and output a classification table with category,
priority, assigned team, and a draft first response for each.
Input: 5 support tickets from the past hour.
Output:
| # | Subject | Category | Priority | Team | Suggested Response |
|---|---|---|---|---|---|
| 1 | "Can't login since this morning" | bug | P1 | Engineering Triage | "Hi Sarah, I'm sorry you're locked out. I've escalated this to our engineering team and they're investigating now. As a workaround, try clearing your browser cache or using an incognito window. We'll update you within 2 hours." |
| 2 | "How do I export data to CSV?" | how-to | P3 | Customer Education | "Hi James, great question! You can export to CSV from Settings → Data → Export. Here's our step-by-step guide: [link]. Let me know if you hit any snags." |
| 3 | "Charged twice for March" | billing | P2 | Finance Support | "Hi Priya, I apologize for the duplicate charge. I've flagged your account for review and our billing team will process the refund within 3-5 business days. You'll receive a confirmation email." |
| 4 | "Would love Slack integration" | feature-request | P3 | Product Management | "Hi Tom, thanks for the suggestion! A Slack integration is something we're hearing a lot about. I've added your vote to our feature tracker and will notify you if it moves to our roadmap." |
| 5 | "Dashboard is down for our entire org" | bug | P0 | Engineering Triage | "Hi Alex, this is our top priority right now. Our on-call engineering team has been paged and is actively investigating. Check status.example.com for live updates. I'll follow up within 30 minutes with a status." |
Input: "URGENT: All API requests returning 500 errors since 14:32 UTC. Affecting our production environment. 50,000+ end users impacted. Enterprise plan."
Output:
spam, auto-close, and exclude from SLA metrics.