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Pm Feature

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konglong87
pm-feature

Use when: 需要细化功能实现细节、拆解复杂功能、为开发准备详细任务、功能点需多维度描述 Do NOT use when: 功能已足够简单可直接进入开发、仅需功能列表无需深度拆解

Overview

Publisherkonglong87
RepositorysuperPM
Skill namepm-feature
Stars
86
Forks
8
Bundled files
1
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.

  • 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 konglong87 on GitHub. Read the source before you install it.

Installation

Install the Pm Feature 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/02-solution-design/pm-feature
  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/02-solution-design/pm-feature .claude/skills/pm-feature
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Feature 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 Feature 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 Feature 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
echo "📊 正在检查前置文档..."
if [ -f "docs/02-方案设计/PRD产品需求文档.md" ]; then
  echo "✅ PRD文档 - 已找到"
else
  echo "⏳ PRD文档 - 未找到"
fi
if [ -f "docs/02-方案设计/原型设计方案.md" ]; then
  echo "✅ 原型设计方案 - 已找到"
else
  echo "⏳ 原型设计方案 - 未找到"
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: 选择需要拆解的功能

您希望拆解哪个功能?

A) 拆解所有核心功能(基于PRD功能列表) B) 拆解单个功能(请指定功能名称) C) 拆解某个模块的所有功能 D) 其他(请手动输入)

💡 提示:开发准备→拆解所有,技术评审→拆解单个复杂功能,项目排期→拆解某模块

记录到变量 FEATURE_SCOPE


步骤 2: 读取前置数据

必需文档:PRD产品需求文档、原型设计方案(需其一) 可选文档:技术对接方案、MVP方案

如果 PRD 不存在:

⚠️ 未找到 PRD 文档

A) 执行 /pm-docs 生成 PRD B) 使用 MVP 方案作为输入 C) 手动输入功能需求(快速模式)


步骤 3: 功能列表提取

从 PRD 提取功能模块划分、功能需求列表、优先级。 从原型设计提取页面功能、交互功能、状态功能。

📋 基于PRD识别出以下功能列表:

用户模块:用户注册、登录、个人信息管理 业务模块:商品浏览、搜索、购物车、订单管理

是否拆解所有功能?

A) 是的,拆解所有 B) 只拆解部分 C) 需要调整功能列表


步骤 4: 功能拆解(逐个功能)

对每个功能使用拆解模板:

4.1 功能基本信息

📝 {功能名称} 功能描述:{从PRD提取} 优先级:P0/P1/P2 用户价值:{解决什么用户问题}

4.2 用户场景分析

🎯 使用动机: A) 完成核心任务 B) 解决具体问题 C) 满足情感需求 使用场景: A) 日常高频 B) 低频但重要 C) 偶发场景

4.3 功能流程拆解

主流程:前置条件→操作步骤序列(6-12步)→后置操作 分支流程:对每个异常场景:触发条件→系统处理→用户反馈→后续操作

4.4 界面元素拆解

🎨 页面结构:顶部/主体/底部元素 交互元素:输入框(校验规则)、按钮(状态)、列表(分页) 状态展示:加载状态/空状态/错误状态

4.5 数据逻辑拆解

💾 输入数据:字段名/类型/必填/校验规则 数据处理:存储位置/更新逻辑/删除策略 数据校验:格式校验/业务校验/权限校验

4.6 异常处理

前端异常(网络/校验/超时)和后端异常(参数/业务/系统)的处理方案 + 边界条件

4.7 技术实现要点

🔧 前端实现:组件选择、状态管理、性能优化 后端实现:API设计、数据库操作、缓存策略 第三方依赖:{服务名称} - {用途}

4.8 测试要点

功能测试(正常/异常/边界)、性能测试、安全测试、兼容性测试


步骤 5: 输出功能拆解文档

使用 Write 工具创建 docs/02-方案设计/功能细节拆解.md。

参考 references/output-template.md 模板,文档结构:

  1. 功能列表总览 - 模块划分、依赖关系
  2. 各模块功能拆解 - 每个功能的完整拆解(基本信息/场景/流程/界面/数据/异常/技术/测试/排期)
  3. 附录 - 开发任务清单、技术风险识别

Subagent 并行拆解(可选)

如果有大量功能(>5个),可使用 Agent 工具并行拆解:

markdown
Agent 1: 拆解用户模块的所有功能
Agent 2: 拆解业务模块的所有功能
Agent 3: 拆解订单模块的所有功能

每个 subagent 按拆解模板输出到文档对应章节
主 agent 等待所有完成,整合输出最终文档

兜底机制

场景: 无前置文档

提供快速模式,用户手动输入功能名称和描述,AI 按模板自动拆解。


注意事项

  1. 核心拆解模板包含8个维度:基本信息→场景→流程→界面→数据→异常→技术→测试
  2. 每个功能拆解完成后记录开发排期(前端/后端/第三方/测试)
  3. 完整输出模板详见 references/output-template.md
  4. 大量功能可使用 subagent 并行拆解提升效率

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

推荐下一步

建议执行:

  1. /pm-user-story - 将功能拆解为具体用户故事
  2. /pm-journey - 绘制用户旅程地图,验证功能覆盖
  3. /pm-mvp - 根据功能拆解重新审视 MVP 范围

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。


Bundled files

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

Frequently asked questions

What does the Pm Feature AI skill do?

Use when: 需要细化功能实现细节、拆解复杂功能、为开发准备详细任务、功能点需多维度描述 Do NOT use when: 功能已足够简单可直接进入开发、仅需功能列表无需深度拆解

Why use Pm Feature on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/konglong87/superPM/tree/main/skills/02-solution-design/pm-feature. 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 Pm Feature?

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 Feature?

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

Is the Pm Feature 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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