自动化客服（基于 FAQ 的轻量检索回复 + 简单转人工/工单接口；支持 Webhook 接入、邮件/企业微信/工单系统集成与可选升级到 LLM 混合模式）。

This skill appears to do what it says: a tiny FAQ-based webhook server. Before running or deploying it, consider: 1) Do NOT run the bundled server in production as-is — it starts Flask with debug=True and host=0.0.0.0, which can expose an interactive debugger and allow remote code execution if internet-accessible. Change debug=False and bind to localhost or run behind a proper WSGI server (gunicorn/uwsgi) and reverse proxy. 2) The code reads an optional CONFIDENCE_THRESHOLD env var but the metadata doesn't declare it — set it explicitly if you need a non-default value. 3) The integration guide mentions external credentials (WeChat/SMTP/Zendesk); those are not used by the sample code but will be required when you implement channel integrations — keep those secrets in environment variables or a secrets manager, and do not paste them into chat. 4) Treat the provided data and templates as examples only (remove sample data before production). 5) Fix the small packaging/run instructions (typo like 'uv pip install flask') and consider adding a proper install/run README and production deployment notes. If you plan to let the agent create/start the service in your workspace, make sure the workspace network exposure and runtime settings are appropriate (no public debug server).

Type: OpenClaw Skill Name: auto-customer-support Version: 1.0.0 The skill bundle provides a legitimate customer support automation scaffold using a Flask-based webhook and a CSV-backed FAQ system. The code in `scripts/server.py` implements simple keyword matching and provides endpoints for message handling and escalation, while `SKILL.md` and `references/integration-guides.md` offer clear instructions and security advice (e.g., avoiding hardcoded credentials). Although the server is configured to run with `debug=True` and binds to `0.0.0.0`, these are standard development settings for a scaffold and do not indicate malicious intent or intentional exploitation.