五子棋理论决策框架

五子棋理论决策框架 is a skill for Claude Code, Codex from R2h1/my-book-skills. It costs 94 tokens per session (1,885 once invoked), scanned A, original, MIT.

A long-term decision framework that treats choices like connected moves in Gomoku, a board game where players aim to make five stones in a row.

In plain words
What is it for?
Use it for career choices, business direction, resource allocation, and plans where a lower short-term reward may create more opportunities later.
Why use it?
It shifts attention from immediate gains to whether a choice builds useful connections and future options.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for career choices, business direction, resource allocation, and plans where a lower short-term reward may create more opportunities later.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/r2h1/my-book-skills/v05
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add R2h1/my-book-skills --skill v05
Clone the repo
git clone --depth 1 https://github.com/R2h1/my-book-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

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README.md
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Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,885 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00094 $0.01885
Opus 5 $0.00047 $0.00942
Sonnet 5 $0.00019 $0.00377
Haiku 4.5 $0.00009 $0.00188

Measured 11d ago against content hash cb8bbc7366cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

五子棋理论决策框架 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

30岁人生开挂7步法/v05-五子棋理论决策框架/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

五子棋理论决策框架

R — 原文

"我把这种不执着于眼前的利益和收入而长期下棋的方式,称为'五子棋理论'。人生就是一场长达百年的漫长游戏。"

— 金承焕, 第五章


I — 方法论骨架

五子棋理论是用'下棋思维'做人生决策的框架。核心不关注'这一步能赚多少',而是'这一步能和已有棋子形成什么连接':

  1. 连珠思维 — 每个决策不只是独立事件,而是与已有技能/资源/人脉连接的棋子。好棋会互相连接形成多条攻击路线。
  2. 短期亏损容忍 — 有价值的棋可能短期看不到收益,但只要后续能连接3个以上新方向,就是好棋。
  3. 布局而非贪子 — 不争一子得失,争全局胜负。有些高时薪的工作可能是坏棋(封闭未来可能性),有些看似赔本的尝试可能是好棋(打开新赛道)。
  4. 百年游戏 — 人生是超长期游戏,3-5年的布局期在百年尺度上只是开局几步。

核心思维转换:从'这个月能赚多少'转向'这一步能和什么连接'


A1 — 书中的应用

案例 1: 作者放弃短期高收入做YouTube

  • 问题: 启动YouTube短期看是'赔本买卖'(放弃每小时90万韩元的咨询收入)
  • 方法论的使用: 作者认为这是五子棋理论的'长远之着'——视频一旦发布就持续产生价值,且能连接作者、出版社、人才等资源
  • 结论: 短期亏损换来长期品牌溢价
  • 结果: YouTube爆红后品牌价值飙升,自动收入增加3倍以上,接到100多家出版社出书邀请

案例 2: 欲望书吧的逆袭

  • 问题: 接手一个生意惨淡的书吧(月销售额不到90万)
  • 方法论的使用: 不装修不营销,只做SEO排名和Instagram更新——打基础而非赚快钱
  • 结论: 基础建设型棋步虽然短期收益低,但长期价值大
  • 结果: 2个月后月销售额突破2000万,成为江南区评分前1-2名的咖啡厅

案例 3: 承浩的三年布局

  • 问题: 上班族想实现财富自由
  • 方法论的使用: 先存钱→学拍卖→买房加价卖→积累资本→开创无人咖啡厅——每一步都是下一步的棋子
  • 结论: 每一步都在为后续棋步铺路
  • 结果: 34岁拥有13家无人学习咖啡厅、土地和葡萄酒酒吧

A2 — 触发场景

用户会在什么情境下需要这个 skill?

  1. 用户面临职业选择或跳槽决策
  2. 用户纠结于'做A赚得多但学不到东西'vs'做B学得多但收入低'
  3. 用户在做年度/季度规划时

语言信号

  • '我不知道该选哪个方向'
  • '这个工作赚钱多但没什么成长'
  • '我想做XX但短期内不赚钱'
  • '别人都在做那个,我要不要跟?'

与相邻 skill 的区分

  • 概率博弈决策框架的区别: 五子棋理论用于中长期战略布局(选择什么方向),概率博弈用于具体决策执行(给定选项后如何下注)。前者问'下哪步棋',后者问'怎么下好这步棋'
  • 提坦道具组合策略的关系: 提坦道具是五子棋中的'棋子',五子棋理论是'下棋策略'

E — 可执行步骤

当 skill 被激活后,agent 应按以下步骤执行:

  1. 列出当前所有'棋子'

    • 帮用户盘点已有资源(技能/人脉/资产/知识)
    • 每项评估:这是士兵级(换时间)、将军级(高时薪)还是指挥者级(被动收入)?
    • 完成标准: 用户有一份清晰的资源清单
  2. 评估候选选项的'连珠潜力'

    • 对每个备选方案问:'这个选择能和我现有的哪个棋子连接?'
    • '这个选择在未来3年能打开多少条新攻击路线?'
    • 完成标准: 每个选项标注了'连珠数'(能与几个已有棋子连接)
  3. 做出布局决策

    • 优先选择连珠数最高的选项(即使短期收益最低)
    • 如果所有选项连珠数都低,建议先花时间积累棋子(学习新技能/建立人脉)
    • 完成标准: 用户确定一个明确的布局方向

B — 边界

不要在以下情况使用此 skill

  • 用户急需现金维持生存时——五子棋理论的前提是有余力布局
  • 日常琐碎选择(今天吃什么)——大炮打蚊子
  • 用户面临时间紧迫的危机时(如被裁员需一周内找到工作)

作者在书中警告的失败模式

  • ce18: 贪图眼前利益忽视长远布局——永远在忙但原地踏步
  • ce13: 健身类比失败——用低效方法追求财富自由,模仿极端而不考虑长期可持续

作者的盲点 / 时代局限

  • 五子棋理论假设用户有'余粮'可以布局——对月光族或负债者来说,'长期布局'是奢侈品
  • 作者所处的时代(YouTube红利期、韩国房地产上升期)放大了他的'长远布局'的回报率
  • 没有讨论如何识别'坏棋'——不是所有短期亏损的决策都是好布局,有些纯粹是失败决策

Read the full file on GitHub · 141 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 141 lines · 94 tokens per session scan A cb8bbc7366cb

Subscribe to this mod's changes

五子棋理论决策框架 is a skill published in the GitHub repository R2h1/my-book-skills (2 stars, last pushed 26d ago), licensed MIT. It adds 94 tokens to every session and 1,885 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.