Customer Testimonial

Community

kangarooking

customer-testimonial

当用户需要为产品文案选择和编排顾客评价/口碑素材时调用此 skill。适用场景：电商详情页、落地页、社交媒体推广中需要用真实用户反馈增强说服力。不适用于：产品功能说明、品牌故事、技术参数描述。关键 trigger 信号：用户提到"好评""口碑""用户反馈""证言""社会证明""买家秀"。此 skill 包含畅销暗示技巧——大企业列销量数据，中小企业讲生动细节。

Overview

Publisher	kangarooking
Repository	`viral-copywriting-skill`
Skill name	`customer-testimonial`
Stars	80
Forks	12
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by kangarooking on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/kangarooking/viral-copywriting-skill/tree/main/customer-testimonial
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.

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/kangarooking/viral-copywriting-skill.git /tmp/viral-copywriting-skill
mkdir -p .claude/skills
cp -r /tmp/viral-copywriting-skill/customer-testimonial .claude/skills/customer-testimonial

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Testimonial 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 Testimonial 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 Testimonial 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.

顾客证言精选法

R — 原文 (Reading)

很多文案编辑意识到要用顾客证言，于是他们在详情页上放满密密麻麻的好评截图，看起来很有气势，实际上效果不好。原因很简单：大部分好评都是泛泛而谈，"很好用""质量不错"，没有击中顾客的核心需求。精选的证言必须击中"核心需求"——顾客花钱最想满足的那个需求。

— 关健明, 第一章 1.6

I — 方法论骨架 (Interpretation)

顾客证言精选法的核心逻辑是：用真实用户的嘴替你说出产品最核心的卖点。

识别核心需求：先搞清楚目标顾客花钱最想满足的那个需求是什么，不是所有需求，是最核心的那一个。
精选而非堆砌：从大量好评中筛选出直接击中核心需求的证言，宁精勿滥。一条精准证言胜过十条泛泛好评。
证言编排：把最有力的一条放在最显眼位置，其他按支持力度递减排列。
畅销暗示融合：如果产品销量大，用具体数据（"10万+家庭的选择""连续3年品类销量第一"）；如果销量还不够大，用生动细节（描述使用场景、前后变化）来传递"很多人在用"的感觉。
证言真实性：保留顾客的原始语言风格和口语感，过度润色反而降低可信度。

A1 — 书中的应用 (Past Application)

案例 1: 水蜜杏品审团

问题: 水蜜杏是一款小众水果，消费者对口感缺乏认知，需要用证言建立信任。
方法论的使用: 组建"品审团"，邀请多位真实消费者品尝后写评价，精选击中"甜度高、果肉细腻"这一核心需求的证言。
结论: 证言直接描述口感体验，如"咬下去全是甜的""比市面上任何杏都甜"，精准击中核心需求。
结果: 消费者对产品口感建立明确预期，转化率显著提升。

案例 2: 玻尿酸原液

问题: 玻尿酸原液属于功效型护肤品，消费者对效果半信半疑。
方法论的使用: 精选多条真实用户评价，聚焦"保湿持久""皮肤变嫩"这一核心需求，证言中包含具体使用周期和感受变化。
结论: 真实用户用自己的话描述效果变化，比品牌自夸更有说服力。
结果: 证言区的点击率和停留时间大幅提升，促成更多下单。

案例 3: 以色列脱毛仪（反面案例）

问题: 脱毛仪详情页堆砌了大量泛泛好评截图，"很好用""很满意"之类。
方法论的使用（失败）: 没有精选，所有好评一股脑贴上去，大部分好评没有击中"脱毛干净、不痛"这个核心需求。
结论: 密密麻麻的好评截图看似有气势，实际顾客扫一眼就跳过了，因为没有一条说到他心坎上。
结果: 转化效果远不如精选少量精准证言的竞品。

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

正在编写电商详情页，需要决定放哪些用户评价。
产品有大量好评但不知道选哪几条放在文案里。
想让口碑素材发挥最大说服力，而不是简单堆砌好评截图。
小众新品缺乏销量数据，需要用细节证言传递"畅销感"。
需要将分散的好评整理成有逻辑的证言序列。

语言信号

"好评这么多，我该选哪几条放详情页？"
"怎么让用户评价更有说服力？"
"我的产品口碑很好，但转化率不高"
"需要加一些社会证明"
"怎么暗示我的产品很畅销？"
"买家秀怎么编排效果最好？"

与相邻 skill 的区分

与 authority-transfer 的区别: authority-transfer 借用的是权威机构/专家的背书，customer-testimonial 借用的是普通消费者的真实体验。前者强调"权威认可"，后者强调"和你一样的人都在用"。
与 factual-evidence 的区别: factual-evidence 用客观数据和实验证明产品性能，customer-testimonial 用主观体验和感受传递产品价值。前者是理性证明，后者是情感共鸣。

E — 可执行步骤 (Execution)

识别核心需求
- 完成标准: 用一句话写出目标顾客花钱最想满足的需求，如"花299元最想买到的是——脱毛干净且不痛"。
收集并筛选证言
- 完成标准: 从所有好评中筛出直接提到核心需求的评价，至少找到3-5条。淘汰所有"很好用""质量不错"等泛泛好评。
编排证言顺序
- 完成标准: 最有力的一条放首位（有具体细节、有前后对比、有情感共鸣），其余按说服力递减排列。每条证言保留原始口语感。
融入畅销暗示
- 完成标准: 如果有销量数据，在证言区开头或结尾用具体数字暗示畅销（如"已有12,847位妈妈选择"）；如果没有销量数据，用证言的数量和生动细节暗示"很多人在用"。
最终检查
- 完成标准: 通读整组证言，确认：(1) 每条都击中核心需求；(2) 读起来像真人在说话；(3) 整体传递"这产品确实好，很多人在用"的感觉。

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

产品完全没有真实用户评价时（无法精选，需先积累种子用户）。
B2B大宗采购决策中（企业采购更看重权威认证和数据，而非个人证言）。
产品存在大量差评时（精选少量好评与大量差评形成反差，反而降低信任）。
证言涉及医疗效果声称时（可能违反广告法）。

作者在书中警告的失败模式

堆砌泛泛好评: 把所有"很好用""不错"的好评全贴上去，看似有气势，实际没有一条击中核心需求，顾客扫一眼就跳过。
过度润色证言: 把顾客评价改得太工整、太书面，失去真实感，顾客一看就知道是编的。
证言与核心需求脱节: 选的证言说的是次要需求（如包装好看），而不是核心需求（如脱毛干净）。

作者的盲点 / 时代局限

2017年书中案例以淘宝/京东电商为主，未涉及短视频评论、小红书笔记等新媒体证言形式。
未讨论证言的真实性验证问题——在"刷评"普遍的当下，消费者对好评的信任度下降，需要更强的真实性信号。
未涉及视频证言和UGC（用户生成内容）的编排策略。

审计信息

验证通过: V1 ✓ / V2 ✓ / V3 ✓
测试通过率: 待测
蒸馏时间: 2026-05-02

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

test-prompts.json5 KB

Frequently asked questions

What does the Customer Testimonial AI skill do?

Why use Customer Testimonial on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/viral-copywriting-skill/tree/main/customer-testimonial. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Customer Testimonial?

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 Testimonial?

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

Is the Customer Testimonial AI skill free?

Yes. It is published on GitHub by kangarooking 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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Overview