Ticket Triage

使用范围与安全规则

本 Skill 只用于“客服工单自动分类分派”场景，围绕用户当前提供或明确授权处理的材料完成任务。功能目标：自动对客服工单进行意图识别、实体提取、优先级分类和团队路由，同时生成初步同理心回复草稿。

• 默认先在回复中交付分析、方案、草稿、代码建议或检查清单，不主动读取任务范围外的文件、账号和数据。
• 信息不足时先标记缺口并向用户确认，不用猜测内容填补事实、数字、姓名、结论或权限。
• 需要联网检索或调用接口时，先说明访问目标、拟发送的数据和用途并取得用户明确同意；只访问用户指定或公开合法来源，不绕过登录、付费墙、验证码、访问控制或平台限制。
• 涉及发送、发布、上传、删除、支付、投放、部署、同步或其他外部写入时，先展示目标、内容和影响，获得用户对该次动作的明确确认后再执行。
• 不要求用户在对话里粘贴密码、Cookie、Token、API Key 或私钥；凭据仅用于用户指定的对应官方服务，不回显、不记录、不写入交付物，也不转发到无关地址。
• 营销与增长输出只做合规方案、内容草稿和测试建议，不承诺流量、排名、成交或收益；不刷量、不群发骚扰、不伪造背书，不设计规避平台规则的做法。
• 交付前复核事实依据、隐私、版权、平台规则和可执行边界；发现高风险或越权请求时停止相关动作，并给出合规替代方案。

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.

Workflow

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

Usage

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.

Examples

Example 1: Batch triage of 5 tickets

Input: 5 support tickets from the past hour.

Output:

Example 2: Urgent production outage escalation

Input: "URGENT: All API requests returning 500 errors since 14:32 UTC. Affecting our production environment. 50,000+ end users impacted. Enterprise plan."

Output:

• Category: bug
• Priority: P0 (production outage, multiple end users, enterprise account)
• Team: Engineering Triage → Immediate page to on-call SRE
• Escalation path: Auto-notify VP Engineering, assigned CSM, and account executive
• SLA clock: 15-minute first response, 1-hour status update cadence
• Draft response: "Hi — this is marked as our highest priority. Our SRE team has been paged and is investigating the 500 errors starting at 14:32 UTC. We'll provide our first status update within 15 minutes. For real-time updates, monitor status.example.com. I'm your point of contact until this is resolved."

Best Practices

• Always extract customer account tier before assigning priority — enterprise customers with production impact should never be lower than P1.
• Use the secondary category tag for tickets that span billing and bug (e.g., "I was charged for a feature that doesn't work") to ensure both teams have visibility.
• Pre-populate the initial response with relevant KB article links by matching extracted entities against your knowledge base index.
• Re-triage tickets that receive a customer reply within 1 hour, as additional context often changes the category or priority.
• Log triage decisions with reasoning so the model can be fine-tuned on corrections from human reviewers.
• Set up auto-escalation rules: any P0 not acknowledged within 15 minutes should page the on-call manager.

Edge Cases

• Multi-intent tickets — A single ticket contains both a bug report and a billing dispute. Classify under the higher-priority intent and create a linked ticket for the secondary issue.
• Spam or vendor outreach — Tickets that are marketing emails or vendor pitches routed through support. Classify as spam, auto-close, and exclude from SLA metrics.
• Non-English tickets — Detect the language, translate for triage, and route to the appropriate language-specific support queue if one exists. Note the original language in internal metadata.
• Repeat tickets from the same customer — Detect duplicate or follow-up tickets within a 24-hour window. Merge into the existing thread rather than creating a new triage entry.
• Tickets with only attachments — When the body is empty but a screenshot or log file is attached, flag for manual review rather than auto-classifying with low confidence.

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