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Pm Mvp

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konglong87
pm-mvp

Use when: 需要确定第一版产品功能范围、已有需求清单需筛选 MVP 功能、需要确定最小可行产品边界 Do NOT use when: 产品已上线需要完整功能集、仅需梳理需求无需裁剪范围

Overview

Publisherkonglong87
RepositorysuperPM
Skill namepm-mvp
Stars
86
Forks
8
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 konglong87 on GitHub. Read the source before you install it.

Installation

Install the Pm Mvp 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/konglong87/superPM/tree/main/skills/01-demand-insight/pm-mvp
  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/konglong87/superPM.git /tmp/superPM
mkdir -p .claude/skills
cp -r /tmp/superPM/skills/01-demand-insight/pm-mvp .claude/skills/pm-mvp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Mvp 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 Pm Mvp 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 Pm Mvp 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.

Preamble (run first)

bash
# 检查是否有优先级排序报告
if [ ! -f "docs/01-需求调研/优先级排序报告.md" ]; then
  echo "⚠️  未找到优先级排序报告"
  echo ""
  echo "建议先执行 /pm-priority 排序需求"
  echo ""
  echo "您可以选择:"
  echo "A) 执行 /pm-priority 先排序需求(推荐)"
  echo "B) 手动选择MVP功能(快速模式)"
fi

写入时边界

只讨论时不写文件,启动检查不创建目录。用户要求保存产物时,先确认当前产品、精确输出路径及已有文件的覆盖/追加选择;确认后仅在实际 Write 前创建该输出文件的父目录。完整安装可参见 skills/_shared/artifact-policy.md,单独安装本 skill 也按本段执行。


跨 Agent 交互规则

当流程要求与用户交互时:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 已有 docs 文件不能替代本轮用户回答。

执行流程

步骤 1: 读取前置数据

使用 Read 工具读取:

  • docs/01-需求调研/优先级排序报告.md(提取P0需求)
  • docs/01-需求调研/需求调研报告.md(提取痛点)

步骤 2: 选择MVP模式

使用 AskUserQuestion:

🎯 选择MVP模式:

A) 最小MVP - 仅核心功能,快速验证(2-4周) B) 标准MVP - 核心功能+基础体验(1-2月) C) 全链路MVP - 完整用户流程(2-3月) D) 自定义MVP - 我来选择功能


步骤 3: 确定核心功能集

如果是最小MVP:

选择P0级需求中最核心的3-5个功能。

如果是标准MVP:

选择全部P0级需求。

如果是全链路MVP:

选择P0+部分P1需求,覆盖完整用户流程。

如果是自定义:

逐个询问每个需求是否纳入MVP。


步骤 4: 风险评估

AI评估MVP方案的风险:

技术风险:

  • 是否有技术难点?
  • 是否需要新技术栈?

业务风险:

  • 市场窗口是否足够?
  • 竞品是否会抢先?

资源风险:

  • 团队是否有足够人力?
  • 预算是否充足?

步骤 5: 生成MVP方案

使用 Write 工具创建 docs/01-需求调研/MVP方案.md:

markdown
# MVP方案

## 一、MVP概述

- **MVP模式**: {模式名称}
- **目标上线时间**: {时间}
- **核心验证目标**: {验证什么}
- **生成时间**: {当前时间}

---

## 二、核心功能集

| 序号 | 功能名称 | 优先级 | 工作量 | 负责人 |
|------|----------|--------|--------|--------|
| 1 | {功能1} | P0 | {工作量} | 待定 |
| 2 | {功能2} | P0 | {工作量} | 待定 |
| 3 | {功能3} | P0 | {工作量} | 待定 |

---

## 三、用户流程

### 3.1 核心用户旅程

用户进入 → {步骤1} → {步骤2} → {步骤3} → 完成


### 3.2 关键触点

- 触点1: {描述}
- 触点2: {描述}

---

## 四、技术方案

### 4.1 技术栈

- 前端: {技术}
- 后端: {技术}
- 数据库: {技术}

### 4.2 关键技术点

1. {技术点1}
2. {技术点2}

---

## 五、风险评估

### 5.1 技术风险

**风险**: {描述}
**应对**: {方案}

### 5.2 业务风险

**风险**: {描述}
**应对**: {方案}

### 5.3 资源风险

**风险**: {描述}
**应对**: {方案}

---

## 六、里程碑规划

| 里程碑 | 时间 | 交付物 |
|--------|------|--------|
| 设计完成 | Week 1 | 设计稿、原型 |
| 开发完成 | Week 2-3 | 功能代码 |
| 测试完成 | Week 4 | 测试报告 |
| 上线 | Week 5 | 产品上线 |

---

## 七、成功标准

### 7.1 业务指标

- 用户数: {目标}
- 留存率: {目标}
- 核心行为: {目标}

### 7.2 技术指标

- 性能: {目标}
- 稳定性: {目标}

---

## 八、下一步建议

建议执行:

1. **/pm-docs** - 生成PRD文档(推荐)
2. **/pm-proto** - 原型设计
3. **/pm-tech** - 技术对接方案

---

**项目状态**: MVP规划完成
**生成时间**: {时间戳}
**生成工具**: super-pm

步骤 6: 输出完成提示

使用 AskUserQuestion:

✅ MVP方案完成!

📄 MVP方案已生成:docs/01-需求调研/MVP方案.md

🎯 建议下一步:

A) 执行 /pm-docs - 生成PRD文档(推荐) B) 执行 /pm-proto - 原型设计 C) 执行 /pm-tech - 技术对接方案 D) 查看MVP方案


兜底机制

场景 1: 无优先级报告

提供快速模式,手动选择MVP功能。

场景 2: 功能过多

提醒用户精简功能,聚焦核心。


注意事项

  1. 聚焦核心验证:MVP要验证核心假设
  2. 控制范围:避免范围蔓延
  3. 快速迭代:尽快上线获取反馈
  4. 风险评估:提前识别风险并制定应对方案
  5. Markdown存储:MVP方案人类可读可编辑

输出质量对比

✅ Good 示例:

- 有数据引用:「根据 Q4 数据,留存率从 35% 降至 28%」
- 有验证来源:「数据来源:Google Analytics, 2025-12-01」
- 有明确建议:「建议将新手引导步骤从 5 步减少至 3 步」

❌ Bad 示例:

- 模糊结论:「数据表明留存率有所下降」
- 无来源:「根据经验,这个功能很重要」
- 没有行动建议:「留存是个问题」

常见误区 / Red Flags — STOP

出现以下情况立即停止并回溯:

误区正确做法
使用"应该"、"大概"、"看起来"做结论必须基于实际数据和验证
未运行检查就声称已完成先验证,再陈述
因时间紧迫跳过关键步骤没有例外,时间紧更要严格
"这次应该没问题"的想法每次都要重新验证

产出质量检查 / Verification Checklist

  • 前置依赖已满足(输入文档/数据已收集)
  • 核心步骤已全部执行
  • 输出文档已生成到 docs/ 目录
  • 每个判断都有数据/证据支撑
  • 已推荐 2-3 个后续 skill

⚠️ 任何一项未通过 → 补全后再标记完成。


Frequently asked questions

What does the Pm Mvp AI skill do?

Use when: 需要确定第一版产品功能范围、已有需求清单需筛选 MVP 功能、需要确定最小可行产品边界 Do NOT use when: 产品已上线需要完整功能集、仅需梳理需求无需裁剪范围

Why use Pm Mvp on TypingMind?

Because you install it once and use it with any model. Pm Mvp 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 Pm Mvp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/konglong87/superPM/tree/main/skills/01-demand-insight/pm-mvp. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pm Mvp?

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 Pm Mvp?

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

Is the Pm Mvp AI skill free?

Yes. It is published on GitHub by konglong87 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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