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Pm Retro

Community
konglong87
pm-retro

Use when: 迭代结束后需要复盘总结经验教训、改进团队流程、记录改进行动项 Do NOT use when: 迭代尚未结束、团队不需要正式复盘

Overview

Publisherkonglong87
RepositorysuperPM
Skill namepm-retro
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 Retro 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/03-growth-iteration/pm-retro
  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/03-growth-iteration/pm-retro .claude/skills/pm-retro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Retro 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 Retro 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 Retro 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

bash
echo "📝 迭代复盘工具已启动"

# 检查迭代计划
if [ -f "docs/03-增长迭代/迭代计划.md" ]; then
  echo "✅ 迭代计划 - 已找到"
else
  echo "⏳ 迭代计划 - 未找到"
fi

# 检查迭代完成情况
if [ -f "docs/03-增长迭代/数据报告与用户反馈.md" ]; then
  echo "✅ 数据报告 - 已找到"
else
  echo "⏳ 数据报告 - 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: 复盘准备

使用 AskUserQuestion 询问:

🎯 迭代复盘准备

迭代信息:

  • 迭代编号:第X次迭代
  • 迭代周期:YYYY-MM-DD 至 YYYY-MM-DD
  • 参与角色:产品、开发、设计、QA、运营(可多选)

迭代目标回顾:

本次迭代原定的核心目标是什么?

A) 目标1:{描述} B) 目标2:{描述} C) 目标3:{描述} D) 没有明确目标

记录到变量 ITERATION_INFO


步骤 2: 目标达成评估

📊 目标达成评估

目标1:{目标描述}

  • 完成情况:{X}%
  • 是否达成:✅/🟡/❌
  • 说明:{完成/未完成的原因}

目标2:{目标描述}

  • 完成情况:{X}%
  • 是否达成:✅/🟡/❌
  • 说明:{完成/未完成的原因}

整体完成率:{X}%

需求完成统计:

优先级计划完成未完成完成率
P0XXXX%
P1XXXX%
P2XXXX%

上述数据是否准确?


步骤 3: 收集团队反馈(KPT)

使用 AskUserQuestion 收集 KPT(Keep-Problem-Try):

📝 团队反馈收集(KPT模型)

Keep - 做得好的地方(值得继续保持)

示例:

  • 代码审查流程规范,Bug率降低
  • 每日站会效率高
  • 设计评审及时

请列举做得好的地方(每行一个,输入"完成"结束):

收集到列表 KEEP_ITEMS

Problem - 需要改进的问题

示例:

  • 需求变更频繁,影响开发节奏
  • 测试时间不足,线上Bug较多
  • 跨部门沟通不及时

请列举需要改进的问题(每行一个,输入"完成"结束):

收集到列表 PROBLEM_ITEMS

Try - 尝试的行动方案

针对上述问题,尝试以下行动:

示例:

  • 建立需求变更审批机制
  • 预留20%测试缓冲时间
  • 建立跨部门沟通群,每天同步进展

请列举尝试的行动(每行一个,输入"完成"结束):

收集到列表 TRY_ITEMS


步骤 4: 根因分析

对 TOP 问题(团队反馈最多的)进行根因分析:

🔍 根因分析

TOP问题:{问题描述}

5 Whys 分析:

问题:{问题}
Why 1:{直接原因}
Why 2:{深层原因}
Why 3:{根本原因}
Why 4:{系统性原因}
Why 5:{根因结论}

根因:{根因结论}

解决方案:

  1. {短期措施} - 立即执行
  2. {长期措施} - 持续改进
  3. {预防措施} - 防止复发

步骤 5: 制定改进措施

🎯 改进措施与行动项

行动项负责人截止时间优先级
{行动项1}{负责人}{日期}P0
{行动项2}{负责人}{日期}P1
{行动项3}{负责人}{日期}P1

下个迭代的改进目标:

  • 将P0完成率从{X}%提升至{Y}%
  • 解决TOP问题:{问题} 到 {标准}

步骤 6: 输出迭代复盘报告

使用 Write 工具创建 docs/03-增长迭代/迭代复盘/复盘-v{版本号}.md:

markdown
# 迭代复盘报告 v{版本号}

## 一、迭代概览

- **迭代编号**:第X次迭代
- **周期**:YYYY-MM-DD 至 YYYY-MM-DD
- **参与角色**:{角色列表}

## 二、目标达成

| 目标 | 完成率 | 状态 | 说明 |
|------|--------|------|------|
| {目标1} | X% | ✅/🟡/❌ | {说明} |
| {目标2} | X% | ✅/🟡/❌ | {说明} |
| **整体** | **X%** | | |

## 三、Keep - 做得好的

1. {内容}
2. {内容}
3. {内容}

## 四、Problem - 需要改进的

1. {内容}(根因:{根因})
2. {内容}
3. {内容}

## 五、Try - 尝试的行动

| 行动项 | 负责人 | 截止时间 | 优先级 |
|--------|--------|---------|--------|
| {行动} | {负责人} | {日期} | P0 |
| {行动} | {负责人} | {日期} | P1 |

## 六、数据指标

| 指标 | 迭代前 | 迭代后 | 变化 |
|------|--------|--------|------|
| {指标} | X | Y | ±Z% |

## 七、下个迭代改进目标

1. {目标1}
2. {目标2}

---

**文档状态**: 迭代复盘完成
**生成时间**: {时间戳}
**生成工具**: super-pm

步骤 7: 完成提示

✅ 迭代复盘完成!

📄 已生成:docs/03-增长迭代/迭代复盘/复盘-v{版本号}.md

🎯 建议下一步:

A) 执行 /pm-iteration - 制定下个迭代计划 B) 执行 /pm-roadmap - 更新产品路线图 C) 如果改进措施涉及风险,执行 /pm-risk


兜底机制

场景: 无前置迭代计划

如果没有迭代计划文档,直接进入KPT复盘流程。


注意事项

  1. 安全氛围:复盘不是追责,聚焦流程改进而非个人
  2. 数据说话:目标达成用数据支撑,避免主观判断
  3. 行动驱动:每个问题必须有对应的行动项
  4. 跟踪闭环:下次复盘时先回顾本次行动项完成情况

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 Retro AI skill do?

Use when: 迭代结束后需要复盘总结经验教训、改进团队流程、记录改进行动项 Do NOT use when: 迭代尚未结束、团队不需要正式复盘

Why use Pm Retro on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/konglong87/superPM/tree/main/skills/03-growth-iteration/pm-retro. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pm Retro?

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 Retro?

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

Is the Pm Retro 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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