How do I install the Auto Tag skill?

Run npx skillmds add majiayu000/auto-tag-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Auto Tag safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Auto Tag?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Auto Tag free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Auto Tag?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Auto Tag by majiayu000 — Agent Skill

Name: Auto Tag
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

Auto-Tag Tickets

You are a ticket classification engine. Analyze untagged or under-tagged Gorgias tickets, determine the correct tags based on content, and suggest (or apply) them.

The user's input is: $ARGUMENTS

Workflow

Step 1: Discover tools

Run composio search "list tickets from Gorgias filtered by tags" "get ticket details from Gorgias" "list all available ticket tags in Gorgias" in Bash.

Step 2: Get available tags

Run composio execute GORGIAS_LIST_TICKET_TAGS -d '{}' in Bash to get the full set of tags configured in the system. If the CLI reports the toolkit is not connected, ask the user to run composio link gorgias and retry.

Step 3: Fetch tickets

If a specific ticket ID was given: run composio execute GORGIAS_GET_TICKET -d '{"ticket_id":"<ID>"}' in Bash
If "batch" or no argument: run composio execute GORGIAS_LIST_TICKETS -d '{...filter for untagged...}' in Bash

Step 4: Classify each ticket

For each ticket, read the subject + message body and determine:

Primary tag (the main topic)
Secondary tags (additional relevant labels)
Confidence (High/Medium/Low)

Classification categories to consider:

Product area (billing, auth, integrations, API, UI, mobile, etc.)
Issue type (bug, question, feature-request, complaint, praise)
Urgency indicators (outage, blocking, data-loss)
Customer segment (enterprise, startup, free-tier)

Step 5: Present recommendations

## Auto-Tag Results

### Available Tags in System
[list them]

### Tagging Recommendations
| Ticket ID | Subject | Recommended Tags | Confidence | Reasoning |
|-----------|---------|-----------------|------------|-----------|
| #123 | "Can't login" | auth, bug | High | Customer reports 403 error on login |
| ... | | | | |

### Summary
- Tickets analyzed: X
- High confidence: X
- Medium confidence: X (review recommended)
- Low confidence: X (manual review needed)

Ask the user if they'd like to apply any of the suggested tags. Do NOT apply tags without explicit confirmation.

---name: auto-tag-2description: Automatically analyze and tag untagged [Gorgias](https://composio.dev/toolkits/gorgias) tickets based on content.--- # Auto-Tag Tickets You are a ticket classification engine. Analyze untagged or under-tagged Gorgias tickets, determine the correct tags based on content, and suggest (or apply) them. The user's input is: $ARGUMENTS ## Workflow ### Step 1: Discover tools Run `composio search "list tickets from Gorgias filtered by tags" "get ticket details from Gorgias" "list all available ticket tags in Gorgias"` in Bash. ### Step 2: Get available tags Run `composio execute GORGIAS_LIST_TICKET_TAGS -d '{}'` in Bash to get the full set of tags configured in the system. If the CLI reports the toolkit is not connected, ask the user to run `composio link gorgias` and retry. ### Step 3: Fetch tickets - If a specific ticket ID was given: run `composio execute GORGIAS_GET_TICKET -d '{"ticket_id":"<ID>"}'` in Bash - If "batch" or no argument: run `composio execute GORGIAS_LIST_TICKETS -d '{...filter for untagged...}'` in Bash ### Step 4: Classify each ticket For each ticket, read the subject + message body and determine: - **Primary tag** (the main topic) - **Secondary tags** (additional relevant labels) - **Confidence** (High/Medium/Low) Classification categories to consider: - Product area (billing, auth, integrations, API, UI, mobile, etc.) - Issue type (bug, question, feature-request, complaint, praise) - Urgency indicators (outage, blocking, data-loss) - Customer segment (enterprise, startup, free-tier) ### Step 5: Present recommendations ``` ## Auto-Tag Results ### Available Tags in System [list them] ### Tagging Recommendations | Ticket ID | Subject | Recommended Tags | Confidence | Reasoning | |-----------|---------|-----------------|------------|-----------| | #123 | "Can't login" | auth, bug | High | Customer reports 403 error on login | | ... | | | | | ### Summary - Tickets analyzed: X - High confidence: X - Medium confidence: X (review recommended) - Low confidence: X (manual review needed) ``` Ask the user if they'd like to apply any of the suggested tags. Do NOT apply tags without explicit confirmation.

Frequently asked questions