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F06 Equanimity Decision

Community
ace3000chao
f06 equanimity-decision

当用户说"我太得意忘形了"、"我最近太顺了感觉要出事"、"我因为一点小事就崩溃了"时调用。 也适用于:阶段性复盘(季度/年终)时评估自己的情绪状态、在重大得失后复盘自己的情绪反应。 不适用于:日常情绪倾诉、用户只是想发泄情绪而非做决策、需要立即处理紧急事务。

Overview

Publisherace3000chao
Repositorybook2startup
Skill namef06 equanimity-decision
Stars
93
Forks
14
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 ace3000chao on GitHub. Read the source before you install it.

Installation

Install the F06 Equanimity Decision 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/ace3000chao/book2startup/tree/master/菜根谭skills/skills/f06
  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.

Use it in TypingMind

Enable F06 Equanimity Decision 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 F06 Equanimity Decision 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 F06 Equanimity Decision 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.

宠辱不惊情绪决策框架

R — 原文 (Reading)

"宠辱不惊,闲看庭前花开花落;去留无意,漫随天外云卷云舒。"

— 洪应明, 菜根谭·宠辱不惊 去留无意


I — 方法论骨架 (Interpretation)

把评价权收回到自己手里,不让外部得失定义你的情绪。

这个框架的核心操作是"心理距离化":把得意的事、失意的事、去或留的决定,都当成"自然现象"来看待。花开花落、云卷云舒,是自然现象,不以你的意志为转移,你只能"闲看"和"漫随"。这种态度不是消极,而是把情绪的掌控权从外部拉回到内部。

方法论步骤:

  1. 得意时不狂喜:成功时天然的冲动是庆祝、炫耀、扩张——但这往往是"乐极生悲"的起点。先按下暂停键,不急着做重大决定。
  2. 失意时不沮丧:失败时天然的冲动是否定自己、逃避、放弃——但这往往错过了"败后反成功"的机会。先稳住,不急于下结论。
  3. 用自然现象做心理距离化:想象"此刻得意/失意的事,十年后还重要吗?"——如果十年后不重要,现在就不需要为之激动或崩溃。

一句话总结: 把评价权收回到自己手里,用自然现象做参照物,不让外部得失劫持你的情绪。


A1 — 书中的应用 (Past Application)

案例 1: 塞翁失马焉知非福

  • 问题: 边塞老翁的马跑到了胡地,对普通人来说,这是明确的"失"——损失了一匹马。
  • 方法论的使用: 老翁不用"得与失"来看这件事,而用"祸福相依"的整体视角:马走是祸,但可能带来更好的结果;马回是福,但可能带来更坏的结果。他对每次得失都保持"不惊"的态度。
  • 结论: "宠辱不惊"的智慧在于:当下无法判断这是好还是坏,所以不值得为得失而或喜或悲。
  • 结果: 马带回了胡马(因祸得福),但儿子因骑胡马而摔断腿(因福得祸)——老翁始终以"不惊"的态度面对所有变化,最终在所有变故中都保持了内在的安定。

案例 2: 范蠡功成身退

  • 问题: 范蠡辅佐越王勾践灭吴,功高震主,此时他面临"宠"(极高荣誉和权力)的局面。
  • 方法论的使用: 范蠡识别到"恩里由来生害"(宠深往往生祸害),在极度得意时选择急流勇退,把"宠"看成花开花落般的自然现象——盛极必衰,不值得恋栈。
  • 结论: "宠"时不狂喜,"宠"后不恋栈,才能全身而退。
  • 结果: 范蠡泛舟五湖,后经商成为"陶朱公",善终且富甲一方。相比之下,同时代的文种未能"宠辱不惊",最终被勾践赐死。

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 得意时即将做重大决定:升职、拿到大订单、融资成功、发布好产品,用户兴奋地想快速扩张、加大投入、宣布重大决策。
  2. 失意时陷入负面思维:项目失败、丢单、被拒绝,用户陷入"我不行"、"我没希望"的思维,停止行动或做出逃避决定。
  3. 因外界评价而情绪剧烈波动:别人一句夸奖能高兴好几天,一句批评能难受好几周,情绪完全被外部牵引。
  4. 阶段性复盘:年终/季度复盘时,用户想评估"我这段时间的情绪决策质量如何",是否有"得意忘形"或"失意沮丧"的失误。
  5. 面临"去/留"决定:是否要离开一个工作、结束一段关系、关闭一个项目,用户被情绪主导而非理性评估。

语言信号 (用户的话里出现这些就应激活)

  • "我太得意忘形了"
  • "最近太顺了,感觉要出事"
  • "因为一点小事就崩了"
  • "刚得到了这个,我很激动,想马上扩大规模"
  • "刚失去了这个,我觉得一切完了"
  • "别人说我好我高兴了好几天,说我不好我又难受了好几天"
  • "我在想要不要离开,但情绪还没稳定下来"
  • "我刚尝到甜头,现在有点飘"

与相邻 skill 的区分

  • 与 f01(退让一步决策框架) 的区别:f01 针对"要不要退让"的策略问题;f06 针对"得意/失意时的情绪调节"问题。f01是策略,f06是情绪。
  • 与 f02(以我转物思维模型) 的区别:f02 是更底层的"主客立场转换"(谁在控制我);f06 是"得意失意时如何保持平衡"的具体操作。f02是理论,f06是应用。
  • 与 f12(一念转魔成佛框架) 的区别:f12 针对"情绪即将失控的瞬间"(怒火/欲水正腾沸)如何转念;f06 针对"得意失意后的平稳心态如何保持"。f12是危机干预,f06是日常修炼。

E — 可执行步骤 (Execution)

当 skill 被激活后,agent 应按以下步骤执行:

  1. 确认当前情绪状态

    • 完成标准: 明确说出"你现在是得意状态、失意状态、还是平静状态",并评估情绪强度(1-10)。
    • 判停条件: 若情绪强度>8(极度兴奋或极度崩溃),先让用户"等24小时再做决定",不进入后续步骤。
  2. 用"花开花落"做心理距离化

    • 完成标准: 让用户用"花开花落、云卷云舒"的句式重新描述当前情境,格式:"我的__(得意/失意)就像庭前的花开花落,是自然现象,不值得为之__(狂喜/沮丧)。"
    • 判停条件: 若用户强烈抗拒(如"这不一样,这真的很重要"),则进入下一步的"十年测试"。
  3. "十年测试"——把时间拉远

    • 完成标准: 问用户"十年后,这件事还重要吗?"若不重要,则提示"现在不需要为之情绪激动";若仍重要,则说"正因为重要,才需要更冷静的判断,而不是更激动的情绪"。
    • 完成标准: 基于以上对话,帮助用户得出一个"此刻不该做的事"(如:不要在这个时刻做重大决策、不要在这个时刻对外宣布、不要在这个时刻人际表态)。

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 用户正在经历急性情绪崩溃:如刚收到重大噩耗、创伤性事件,此时"宠辱不惊"是冷漠,不是智慧。应先共情,再谈框架。
  • 用户在决策链中处于信息劣势:如果用户需要做决策但信息不完整(如谈判中对方信息不透明),此时"不惊"可能让用户错过关键时机。
  • 涉及重大人生安全决定:如是否要逃离危险处境、是否要就医,此时不是讨论"不惊"的时候。

作者在书中警告的失败模式

  • 苦心中常得悦心之趣,得意时便生失意之悲(f10):得意时生悲是"宠辱不惊"的反面——太得意就会开始担心失去,反而无法享受当下的成功。
  • 快意不知回首(ce03):春风得意时不知见好就收,贪恋权位不肯急流勇退。表现为处于巅峰时愈发贪婪、不愿接受任何退让、享受众人吹捧不能自拔。

容易混淆的邻近方法论

  • "宠辱不惊" vs "情感隔离/情感麻木":不惊是情绪稳定后的清明,情感隔离是压抑情绪后的空白。前者是能力,后者是防御机制。f06要求的是"看得清楚但不被动摇",不是"感受不到"。
  • "宠辱不惊" vs "无所谓/躺平":不惊是"我选择不让外部评价控制我";无所谓是"我放弃在乎"。前者是主动选择,后者是主动放弃。

相关 skills (阶段 3 填充)

  • depends-on: [f02]
  • contrasts-with: [f01, f12]
  • composes-with: [f10, f20]

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓
  • 测试通过率: N/A (待填充)
  • 蒸馏时间: 2026-04-24

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 F06 Equanimity Decision AI skill do?

当用户说"我太得意忘形了"、"我最近太顺了感觉要出事"、"我因为一点小事就崩溃了"时调用。 也适用于:阶段性复盘(季度/年终)时评估自己的情绪状态、在重大得失后复盘自己的情绪反应。 不适用于:日常情绪倾诉、用户只是想发泄情绪而非做决策、需要立即处理紧急事务。

Why use F06 Equanimity Decision on TypingMind?

Because you install it once and use it with any model. F06 Equanimity Decision 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 F06 Equanimity Decision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/菜根谭skills/skills/f06. 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 F06 Equanimity Decision?

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 F06 Equanimity Decision?

As many as you like. As long as a model supports skills, you can use F06 Equanimity Decision with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the F06 Equanimity Decision AI skill free?

Yes. It is published on GitHub by ace3000chao 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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