customer-support-autopilot

Classify customer support tickets, draft accurate responses, suggest macros, and route escalations based on risk, SLA, and business impact.

3,891 stars

Best use case

customer-support-autopilot is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Classify customer support tickets, draft accurate responses, suggest macros, and route escalations based on risk, SLA, and business impact.

Teams using customer-support-autopilot should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/customer-support-autopilot/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/anugotta/customer-support-autopilot/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/customer-support-autopilot/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How customer-support-autopilot Compares

Feature / Agentcustomer-support-autopilotStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Classify customer support tickets, draft accurate responses, suggest macros, and route escalations based on risk, SLA, and business impact.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Customer Support Autopilot

## Purpose

Improve support response speed and consistency while reducing risk.

## Core capabilities

- classify incoming tickets by intent/severity
- draft response suggestions in brand tone
- propose macro usage and next actions
- route to L1/L2/L3 based on policy
- detect risky cases (legal, security, billing, fraud, abuse)

## Guardrails

- never invent policy promises
- never disclose sensitive internal info
- escalate regulated/high-risk cases immediately
- include reference IDs when available

## Workflow

1. Parse ticket and extract entities.
2. Classify category + urgency.
3. Draft response with confidence level.
4. Recommend escalation path and SLA.
5. Output macro + notes for agent.

## Output format

1. category + severity
2. draft response
3. escalation recommendation
4. SLA target + required follow-up

## Setup

Read [setup.md](setup.md).

## Examples

See [examples.md](examples.md).

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