Customer Support Customer Service logo

Customer Support Customer Service

Community
chendongqi
customer-support-customer-service

客服助手 - 专业的客户服务与支持体系专家。适用场景: (1) 客服话术设计与优化 (2) FAQ知识库构建与维护 (3) 客户问题分类与处理流程 (4) 投诉处理与升级机制 (5) 客户满意度分析与改进 (6) 客服培训材料编写 (7) 自动回复与机器人话术 触发关键词:客服话术、FAQ、客户投诉、客户满意度、知识库、客服培训、自动回复、工单处理、客户咨询、售后服务

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namecustomer-support-customer-service
Stars
133
Forks
25
Bundled files
Instructions only
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 chendongqi on GitHub. Read the source before you install it.

Installation

Install the Customer Support Customer Service 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.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/customer-support-customer-service
  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/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/customer-support-customer-service .claude/skills/customer-support-customer-service
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Support Customer Service 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 Customer Support Customer Service 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 Customer Support Customer Service 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.

客服助手

核心工作流程

1. 客服话术设计

话术设计原则:

基本原则
├── 礼貌尊重:使用敬语、表达感谢
├── 简洁清晰:避免专业术语、表达直接
├── 同理心:理解客户感受、表达理解
├── 解决导向:聚焦问题解决、给出方案
└── 一致性:话术风格统一、口径一致

标准话术结构:

场景开头主体结尾
问候"您好,感谢联系XX客服"自我介绍+询问需求"请问有什么可以帮您?"
解答"感谢您的咨询"问题解答+操作指引"请问还有其他问题吗?"
处理"非常抱歉给您带来不便"解决方案+时间承诺"我们会尽快为您处理"
结束"感谢您的理解与支持"确认问题已解决"祝您生活愉快,再见"

高频场景话术模板:

查询类
"您好,您咨询的[XX问题],情况如下:[具体信息]。
如需进一步了解,您可以[操作指引]。"

操作指引类
"您好,[XX操作]的步骤如下:
1. [步骤1]
2. [步骤2]
3. [步骤3]
如操作过程中遇到问题,随时联系我们。"

安抚类
"非常理解您的心情,遇到这种情况确实让人着急。
请您放心,我们会[解决方案],预计[时间]内为您处理完成。"

拒绝类
"非常抱歉,由于[原因],目前无法[满足需求]。
不过您可以[替代方案],希望能够帮到您。"

2. FAQ知识库构建

知识库结构设计:

一级分类(按业务线)
├── 产品相关
│   ├── 功能使用
│   ├── 购买咨询
│   └── 版本更新
├── 订单相关
│   ├── 下单流程
│   ├── 支付问题
│   └── 订单修改
├── 物流相关
│   ├── 发货查询
│   ├── 配送范围
│   └── 签收问题
├── 售后相关
│   ├── 退换货
│   ├── 维修保养
│   └── 投诉建议
└── 账户相关
    ├── 注册登录
    ├── 密码找回
    └── 账户安全

FAQ编写规范:

要素规范示例
问题用户视角、口语化"怎么退款?"而非"退款流程"
答案步骤清晰、结论先行先说结论再说步骤
关键词多种表达方式退款/退钱/撤销订单
更新定期检查有效性月度Review

3. 问题分类与处理流程

问题分类体系:

按紧急程度
├── P0紧急:系统故障/资金损失/安全问题
├── P1高优:功能异常/订单问题
├── P2中等:使用咨询/功能建议
└── P3低优:一般咨询/非紧急反馈

按处理方式
├── 自助解决:FAQ/帮助文档
├── 一线处理:标准流程可解决
├── 二线升级:需技术/业务支持
└── 特殊处理:VIP/危机/法务

工单处理SLA:

等级首响时间解决时限升级条件
P05分钟2小时立即升级主管
P115分钟4小时超1小时升级
P230分钟24小时超4小时升级
P32小时72小时超24小时升级

4. 投诉处理机制

投诉处理流程:

接收阶段
├── 认真倾听:让客户完整表达
├── 表达理解:同理心回应
├── 记录信息:问题/诉求/情绪
└── 初步安抚:稳定客户情绪

分析阶段
├── 问题定性:有责/无责/待确认
├── 责任判定:产品/服务/物流/第三方
├── 影响评估:损失/舆情风险
└── 方案制定:补偿/解决方案

解决阶段
├── 方案沟通:与客户确认方案
├── 执行落地:按承诺时间完成
├── 结果确认:确认客户满意
└── 闭环归档:记录处理结果

跟进阶段
├── 回访关怀:48小时内回访
├── 问题根因:分析根本原因
├── 流程优化:推动改进
└── 知识沉淀:案例入库

补偿策略参考:

问题类型轻微一般严重
服务态度道歉道歉+优惠券道歉+补偿+回访
产品质量换货退款+优惠券退款+补偿+升级服务
延迟发货道歉运费减免运费退还+优惠券
信息泄露解释+安全保障补偿+安全升级补偿+专人跟进+法务

5. 客户满意度分析

满意度指标体系:

核心指标
├── CSAT(客户满意度):单次服务评分
├── NPS(净推荐值):推荐意愿
├── CES(客户费力度):问题解决难易度
└── FCR(首次解决率):一次性解决比例

过程指标
├── 平均响应时间
├── 平均处理时长
├── 工单解决率
├── 升级率
└── 重复咨询率

满意度分析维度:

维度分析内容改进方向
渠道不同渠道满意度差异优化低分渠道
问题类型哪类问题满意度低优化相关流程
客服人员个人表现差异针对性培训
时段不同时段满意度人力调配优化
客户群体不同客群满意度差异化服务

6. 自动回复与机器人

智能客服话术设计:

欢迎语
"您好,我是XX智能助手小X,很高兴为您服务!
请问有什么可以帮您?您也可以输入关键词快速查询:
【订单查询】【退换货】【优惠活动】【联系人工】"

无法识别
"抱歉,我没有理解您的问题。
您可以换个方式描述,或输入【人工】转接人工客服。"

转人工
"正在为您转接人工客服,请稍候...
当前排队人数:X人,预计等待:X分钟。"

非工作时间
"当前是非工作时间,人工客服服务时间为9:00-21:00。
您可以留下问题,我们会在工作时间第一时间联系您。"

输出模板

话术模板

【场景】___________
【触发条件】___________

【话术】
开场:

主体:

结尾:

【注意事项】
-
-

FAQ模板

【问题】___________
【关键词】___________
【答案】

【相关问题】
-
-

【更新时间】___________

投诉处理记录

【工单号】___________
【客户信息】___________
【问题描述】

【责任判定】□有责 □无责 □待确认
【解决方案】

【处理结果】□满意 □一般 □不满意
【经验总结】

最佳实践

  • 同理心优先:先处理情绪,再处理问题
  • 解决问题为核心:不推诿、不敷衍
  • 授权一线:给一线足够的处理权限
  • 持续改进:从投诉中发现系统问题
  • 数据驱动:用数据指导服务优化

Frequently asked questions

What does the Customer Support Customer Service AI skill do?

客服助手 - 专业的客户服务与支持体系专家。适用场景: (1) 客服话术设计与优化 (2) FAQ知识库构建与维护 (3) 客户问题分类与处理流程 (4) 投诉处理与升级机制 (5) 客户满意度分析与改进 (6) 客服培训材料编写 (7) 自动回复与机器人话术 触发关键词:客服话术、FAQ、客户投诉、客户满意度、知识库、客服培训、自动回复、工单处理、客户咨询、售后服务

Why use Customer Support Customer Service on TypingMind?

Because you install it once and use it with any model. Customer Support Customer Service 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 Customer Support Customer Service in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/customer-support-customer-service. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Customer Support Customer Service?

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 Customer Support Customer Service?

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

Is the Customer Support Customer Service AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. 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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