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Ticket Triage

Community
seb1n
ticket-triage

Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. Use when the user requests ticket triage or provides relevant inputs for this workflow.

Overview

Publisherseb1n
Repositoryawesome-ai-agent-skills
Skill nameticket-triage
Stars
188
Forks
35
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by seb1n on GitHub. Read the source before you install it.

Installation

Install the Ticket Triage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/customer-success/ticket-triage .claude/skills/ticket-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ticket Triage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ticket Triage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ticket Triage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Ticket Triage

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:

#SubjectCategoryPriorityTeamSuggested Response
1"Can't login since this morning"bugP1Engineering 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-toP3Customer 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"billingP2Finance 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-requestP3Product 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"bugP0Engineering 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."

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.

Frequently asked questions

What does the Ticket Triage AI skill do?

Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. Use when the user requests ticket triage or provides relevant inputs for this workflow.

Why use Ticket Triage on TypingMind?

Because you install it once and use it with any model. Ticket Triage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ticket Triage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/ticket-triage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ticket Triage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ticket Triage?

As many as you like. As long as a model supports skills, you can use Ticket Triage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ticket Triage AI skill free?

Yes. It is published on GitHub by seb1n under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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