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Product Naming

Community
yunshu0909
product-naming

产品命名协作流程。当用户想给产品/项目/模块起名字时使用。通过"灵魂挖掘 → 约束提取 → 路线发散 → 方向选择 → 竞品验证 → 最终确认"的结构化流程,从模糊想法产出有品牌生命力的名字,避免拍脑袋起名。

Overview

Publisheryunshu0909
Repositoryyunshu_skillshub
Skill nameproduct-naming
Stars
769
Forks
109
Bundled files
Instructions only
LicenseMIT
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 yunshu0909 on GitHub. Read the source before you install it.

Installation

Install the Product Naming 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/yunshu0909/yunshu_skillshub/tree/master/brand/product-naming
  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/yunshu0909/yunshu_skillshub.git /tmp/yunshu_skillshub
mkdir -p .claude/skills
cp -r /tmp/yunshu_skillshub/brand/product-naming .claude/skills/product-naming
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Naming 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 Product Naming 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 Product Naming 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.

用户想给产品、项目或模块起一个名字,但还没想好叫什么。通过结构化的协作流程,从产品本质出发推导名字,而不是直接列一堆候选词让用户挑。

核心原则

  • 先想清楚再起名 — 没有理解产品灵魂之前,绝不列候选名字
  • 名字是推导出来的,不是拼凑出来的 — 从产品定位、用户画像、品牌气质中自然推导
  • 给路线,不给列表 — 先让用户选命名方向,再在方向内发散具体名字
  • 用数据验证直觉 — 调研同赛道产品的命名规律,避免闭门造车
  • 不催不赶 — 名字是品牌的根基,值得花时间想清楚

工作流程

第 1 步:产品灵魂挖掘(不碰名字)

这一步的目标是理解产品的本质,而不是收集功能列表。

主动了解项目背景(读代码、读文档),然后向用户追问以下问题:

  1. 用户是谁? — 什么人在用?什么场景下用?使用频率?
  2. 产品本质 — 用一句话介绍这个产品是什么?(不是功能列表,是角色/定位/比喻)
  3. 产品边界 — 做什么、不做什么、未来往哪个方向长?
  4. 品牌气质 — 用户看到这个名字时,应该有什么第一反应?(专业/亲切/极客/轻松/...)
  5. 硬性约束 — 语言偏好?长度限制?需要避开的词?

关键:如果用户给出一个比喻(比如"像 Jarvis"),一定要深挖这个比喻背后的含义——它揭示的是用户对产品角色的想象。

禁止:

  • 读完项目文档就开始列名字
  • 问一个问题就觉得够了,必须把以上问题都覆盖到
  • 把这些问题当表单一次性丢给用户,应该是自然对话

第 2 步:命名约束提取

从第 1 步的回答中提取:

  1. 关键词 — 从用户的描述中提炼核心语素(如:编程、助手、伙伴、工具、管理)
  2. 识别张力 — 用户的多个诉求之间是否存在矛盾?(如"要像人名一样有温度" vs "一眼看出跟编程有关")
  3. 明确优先级 — 如果有张力,哪个诉求优先?

向用户展示你的分析,确认理解无误。

示例:
你的核心诉求:
- ✅ 让人知道跟团队协作有关
- ✅ 轻松不严肃,不像企业软件
- ⚡ 这两个有张力:协作类名字容易显得"企业味重",轻松的名字又容易看不出用途

我的判断是"轻松感"优先,因为你要跟 Slack/钉钉竞争的是体验而非功能。对吗?

禁止:跳过分析直接出名字。这一步是从"感觉"到"逻辑"的关键桥梁。

第 3 步:路线发散

基于约束分析,推导出 2-4 条命名路线,每条路线代表一种不同的命名策略。

每条路线包含:

  • 路线名称 — 一句话概括这条路的思路
  • 2-3 个示意名字 — 让用户感受这个方向的味道
  • 优缺点 — 诚实说明每条路的利弊
示例:
路线 A:拟声/动作词
示意:Ping、Holler、Nudge
✅ 轻快、有画面感
❌ 不直接关联"站会"场景

路线 B:时间/节奏隐喻
示意:DayBeat、MorningSync、Cadence
✅ 暗示每日节奏,贴合站会频率
❌ 偏长,组合感强

路线 C:缩写/造词
示意:Stanly(standup + daily)、Asynco(async + co)
✅ 独特好注册
❌ 需要解释含义

禁止:

  • 只给一条路线
  • 路线之间差异太小
  • 不说缺点,只说好话
  • 在这一步列超过 5 个具体名字/路线(让用户选方向,不是选名字)

第 4 步:用户选择方向

用户可以:

  • 直接选一条路线
  • 组合多条路线的元素("C 的直接 + A 的温度")
  • 全部否掉,补充新方向 → 回到第 3 步

选定方向后,在该方向内深挖 3-5 个具体候选名字,每个附带一句话解释。

禁止:用户选了方向之后还在其他方向上发散。聚焦。

第 5 步:竞品验证 + 命名策略

用户倾向某个名字后,做两件事:

5a. 竞品命名调研

搜索同赛道产品的命名规律:

  • 纯英文 / 中英文双品牌 / 纯中文,哪种多?
  • 名字长度?音节数?
  • 有没有撞名风险?
5b. 命名策略确认

基于调研结果,向用户建议:

  • 需要几个名字?(英文名 + 中文名?还是只要一个?)
  • 要不要 slogan?
  • 理由是什么?
示例:
调研发现同赛道的 Geekbot、Range、Standuply 都只用一个英文名。
建议:只用 Ping,不另起中文名。
理由:用户是开发团队,日常沟通已经用英文工具;一个音节最好记。
如果需要中文场景,加 slogan:"Ping — 轻拍一下,站会搞定"

禁止:不做调研就建议命名策略,所有建议必须有数据支撑。

第 6 步:最终确认

汇总整个过程的决策链路,输出最终结论:

📌 产品名称:Ping
📌 Slogan:轻拍一下,站会搞定
📌 命名策略:纯英文单品牌
📌 决策依据:
   - 用户画像:远程开发团队
   - 产品定位:替代每日站会的异步同步工具
   - 核心约束:轻松不严肃,一看就想用
   - 竞品惯例:同赛道主流为纯英文短名称

沟通规范

必须问用户的

时机问什么
第 1 步用户画像、产品本质、产品边界、品牌气质、硬性约束
第 3 步选哪条路线
第 4 步倾向哪个具体名字
第 5 步命名策略是否认同(几个名字、要不要 slogan)
第 6 步最终确认

不需要问用户的

事项直接做
读项目文档了解背景直接读,带着理解去问用户
竞品调研直接搜索,带着数据给建议
分析诉求之间的张力直接分析,向用户确认判断

AI 绝不应该做的

  • 读完项目文档就甩一个名字列表
  • 没有理解产品本质就开始起名
  • 只从"好不好听"角度评价名字,不从品牌策略角度分析
  • 用户说"不好"的时候换一批名字再来,而不是反思流程哪里出了问题
  • 为了显得专业而列过多选项,造成选择瘫痪
  • 催用户赶快做决定

Frequently asked questions

What does the Product Naming AI skill do?

产品命名协作流程。当用户想给产品/项目/模块起名字时使用。通过"灵魂挖掘 → 约束提取 → 路线发散 → 方向选择 → 竞品验证 → 最终确认"的结构化流程,从模糊想法产出有品牌生命力的名字,避免拍脑袋起名。

Why use Product Naming on TypingMind?

Because you install it once and use it with any model. Product Naming 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 Product Naming in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yunshu0909/yunshu_skillshub/tree/master/brand/product-naming. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Product Naming?

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 Product Naming?

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

Is the Product Naming AI skill free?

Yes. It is published on GitHub by yunshu0909 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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