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Pm Okr

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konglong87
pm-okr

Use when: 需要制定 OKR(目标与关键结果)、拆解公司/产品级目标、做目标对齐与 cascading、设定复盘节奏 Do NOT use when: 仅需简单待办清单(用 pm-iteration);目标已由上级完全锁定且无需拆解

Overview

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

Use it in TypingMind

Enable Pm Okr 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 Okr 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 Okr 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
if [ -f "docs/05-产品战略/战略决策建议.md" ]; then
  echo "✅ 战略决策建议 - 已找到(可承接为 O 的来源)"
else
  echo "⏳ 战略决策建议 - 未找到(可选)"
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。

适用场景

  • 用户说"定 OKR""目标拆解""关键结果怎么写""目标对齐""季度目标"
  • 与 pm-iteration(迭代待办)区分:OKR 管方向与可度量结果,iteration 管执行排期。

OKR 基本法(简述)

  • O(Objective):方向性、鼓舞人心、定性、不堆指标。
  • KR(Key Result):可度量、有基线值与目标值、可验证、通常 2-4 个/每 O。
  • 对齐(Cascading):公司 → 产品/团队 → 个人,下层 KR 支撑上层 O。
  • 信心指数:制定时标注 50%(有挑战)/ 70%(较稳),用于复盘。

执行流程

步骤 1: 明确目标来源(主 agent - 用户交互)

使用 AskUserQuestion 询问:

🎯 OKR 范围

这次 OKR 落到哪一层?

A) 产品级 OKR(承接公司/战略) B) 团队级 OKR C) 个人/项目级 OKR

目标来源:

  1. 沿用战略决策建议
  2. 我手动输入业务重点
  3. 基于当前增长/数据现状推导

记录 OKR_LEVEL 与 OBJECTIVE_SOURCE。


步骤 2: 提炼 Objective(主 agent)

基于来源,提炼 1-3 个 O,要求:

  • 一句话方向、鼓舞人心、不含指标
  • 与战略/业务重点一致

示例:「让产品在目标用户中成为首选」「把增长引擎跑通并可持续」

使用 AskUserQuestion 与用户确认 O 的表述与数量。


步骤 3: 拆解为 Key Results(主 agent + 可选 subagent)

为每个 O 拆 2-4 个 KR,遵循:可度量 + 基线 → 目标 + 验证方式。

可选使用 Agent 工具派发 subagent 对复杂主题生成 KR 草案:

Tool: Task
Parameters:
  subagent_type: "general-purpose"
  description: "OKR-KR草案"
  prompt: |
    你是目标管理专家。为以下 Objective 拆解 Key Results。
    O: {O}
    上下文: {产品现状/数据}
    要求:每个 O 2-4 个 KR;每个 KR 含 基线值→目标值、度量方式、信心指数(50%/70%);避免把任务当 KR。
    输出 Markdown。

主 agent 整合并与用户确认。


步骤 4: 生成对齐地图与复盘节奏(主 agent)

  • 对齐地图:上层 O 与下层 KR 的支撑关系
  • 复盘节奏:双周打卡进度、季度复盘评分(0-1.0,0.7 为达标)
  • 风险与依赖:KR 之间的依赖、资源需求

步骤 5: 生成 OKR 文档(主 agent)

使用 Write 工具生成 docs/03-增长迭代/OKR目标管理.md:

markdown
# {产品/团队} OKR({周期})

## 一、Objective 与 Key Results
### O1: {方向性目标}
- KR1: {基线} → {目标},度量: {方式},信心: {50%/70%}
- KR2: ...

### O2: {方向性目标}
- KR1: ...

## 二、对齐地图
| 上层 O | 支撑的下层 KR |
|-------|--------------|
| {公司O} | {产品KR1, KR2} |

## 三、度量与基线
| KR | 当前基线 | 目标 | 数据源 | 负责人 |
|----|---------|------|-------|-------|
| {KR} | {值} | {值} | {源} | {人} |

## 四、复盘节奏
- 双周: 进度打卡
- 季度: 评分复盘(0-1.0,0.7 达标)

## 五、依赖与风险
- 依赖: {列表}
- 风险: {列表}

## 六、下一步建议
1. /pm-iteration - 将 KR 转为迭代待办
2. /pm-retro - 季度复盘
3. /pm-report - 跟踪 KR 进度

步骤 6: 推荐下一步(主 agent)

✅ OKR 已生成:docs/03-增长迭代/OKR目标管理.md

建议执行:

  1. /pm-iteration - KR 转迭代待办
  2. /pm-retro - 季度复盘
  3. /pm-report - 进度跟踪

输出质量对比

✅ Good 示例:

  • KR 可度量:「月活 12w → 20w,数据源 GA,信心 50%」
  • O 不含指标:「让新用户首周留存显著提升」
  • 有对齐与复盘

❌ Bad 示例:

  • 把任务当 KR:「完成新版上线」
  • O 写成指标堆:「DAU 提升 30%」
  • 无基线与复盘

常见误区 / Red Flags — STOP

误区正确做法
把任务当 KRKR 必须可度量、有基线目标
O 写成指标堆O 是方向性定性目标
KR 无基线必须标注基线→目标
不做对齐下层 KR 需支撑上层 O
无复盘必须定义打分与节奏

产出质量检查 / Verification Checklist

  • 已明确 OKR 层级与目标来源
  • O 方向性、不含指标、已确认
  • 每个 O 有 2-4 个可度量 KR(基线→目标)
  • 已生成对齐地图与复盘节奏
  • 输出文档已生成到 docs/03-增长迭代/
  • 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 Okr AI skill do?

Use when: 需要制定 OKR(目标与关键结果)、拆解公司/产品级目标、做目标对齐与 cascading、设定复盘节奏 Do NOT use when: 仅需简单待办清单(用 pm-iteration);目标已由上级完全锁定且无需拆解

Why use Pm Okr on TypingMind?

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

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

Which AI models can use Pm Okr?

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 Okr?

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

Is the Pm Okr 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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