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Pm Pool

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konglong87
pm-pool

Use when: 有新需求需要录入跟踪、需求状态需要变更、需要统一管理需求池、需求数量增多需要结构化 Do NOT use when: 需求极少可直接管理、仅需一次性分析无需持续跟踪

Overview

Publisherkonglong87
RepositorysuperPM
Skill namepm-pool
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 Pool 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-pool
  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-pool .claude/skills/pm-pool
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Pool 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 Pool 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 Pool 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
# 检查是否有需求池
if [ ! -f "docs/01-需求调研/需求池管理表.md" ]; then
  echo "⚠️  未找到需求池管理表"
  echo ""
  echo "将创建新的需求池"
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: 选择操作

使用 AskUserQuestion 询问:

🎯 您要对需求池进行什么操作?

A) 添加新需求 B) 查看需求池 C) 更新需求状态 D) 删除需求 E) 导出需求池


步骤 2: 执行操作


操作 A: 添加新需求

使用 AskUserQuestion 逐个询问:

问题 1: 需求名称

请输入需求名称:

例如:"用户登录"、"搜索功能"


问题 2: 需求类型

需求类型是什么?

A) 新功能 B) 功能优化 C) Bug修复 D) 技术债务 E) 其他


问题 3: 优先级

需求优先级?

A) P0 - 紧急重要 B) P1 - 重要不紧急 C) P2 - 紧急不重要 D) P3 - 不紧急不重要


问题 4: 来源

需求来源?

A) 用户反馈 B) 业务部门 C) 产品规划 D) 竞品分析 E) 其他(请手动输入)


问题 5: 描述

请详细描述需求:

包括背景、目标、验收标准等


AI 将需求信息添加到需求池管理表。


操作 B: 查看需求池

读取 docs/01-需求调研/需求池管理表.md,格式化输出:

📋 需求池概览

需求总数: {N}个 P0: {N}个 P1: {N}个 P2: {N}个 P3: {N}个

按状态分类:

  • 待评估: {N}个
  • 已规划: {N}个
  • 开发中: {N}个
  • 已完成: {N}个

查看详情?


操作 C: 更新需求状态

列出所有需求,让用户选择要更新的需求:

请选择要更新的需求:

A) {需求1} - 当前状态: 待评估 B) {需求2} - 当前状态: 已规划 C) {需求3} - 当前状态: 开发中 ...

用户选择后,询问新状态:

新状态是什么?

A) 待评估 B) 已规划 C) 开发中 D) 测试中 E) 已上线 F) 已关闭

更新需求池管理表。


操作 D: 删除需求

列出所有需求,让用户选择要删除的需求:

请选择要删除的需求:

A) {需求1} B) {需求2} ...

确认后删除。


操作 E: 导出需求池

生成可分享的需求池文档:

选择导出格式:

A) Markdown B) CSV C) JSON


步骤 3: 更新需求池管理表

如果需求池管理表不存在,使用 Write 创建:

markdown
# 需求池管理表

## 一、需求概览

- **需求总数**: {N}个
- **更新时间**: {当前时间}

---

## 二、需求列表

| ID | 需求名称 | 类型 | 优先级 | 状态 | 来源 | 创建时间 |
|----|----------|------|--------|------|------|----------|
| R001 | {需求1} | {类型} | {优先级} | {状态} | {来源} | {时间} |
| R002 | {需求2} | {类型} | {优先级} | {状态} | {来源} | {时间} |

---

## 三、需求详情

### R001: {需求名称}

**类型**: {类型}

**优先级**: {优先级}

**状态**: {状态}

**来源**: {来源}

**描述**:
{详细描述}

**验收标准**:
- {标准1}
- {标准2}

**负责人**: {负责人}

**预计完成时间**: {时间}

---

## 四、统计信息

### 4.1 按优先级

- P0: {N}个
- P1: {N}个
- P2: {N}个
- P3: {N}个

### 4.2 按状态

- 待评估: {N}个
- 已规划: {N}个
- 开发中: {N}个
- 已上线: {N}个

---

**最后更新**: {时间戳}
**生成工具**: super-pm

如果需求池已存在,使用 Edit 更新。


步骤 4: 输出完成提示

使用 AskUserQuestion:

✅ 需求池已更新!

📄 需求池管理表:docs/01-需求调研/需求池管理表.md

🎯 下一步:

A) 继续管理需求池 B) 执行 /pm-priority - 对需求排序 C) 执行 /pm-mvp - 规划MVP D) 查看需求池


兜底机制

场景 1: 需求池不存在

自动创建新的需求池。

场景 2: 需求数量过多

提供筛选和搜索功能。


注意事项

  1. 唯一ID:每个需求分配唯一ID(R001, R002...)
  2. 状态流转:记录状态变更历史
  3. 优先级可调整:根据业务变化调整
  4. Markdown存储:需求池人类可读可编辑
  5. 定期清理:建议定期清理已完成需求

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 Pool AI skill do?

Use when: 有新需求需要录入跟踪、需求状态需要变更、需要统一管理需求池、需求数量增多需要结构化 Do NOT use when: 需求极少可直接管理、仅需一次性分析无需持续跟踪

Why use Pm Pool on TypingMind?

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

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

Which AI models can use Pm Pool?

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 Pool?

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

Is the Pm Pool 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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