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.
npx skills add R2h1/my-book-skills --skill v06git clone --depth 1 https://github.com/R2h1/my-book-skillsWrote 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.
[](https://agentmods.dev/skills/r2h1/my-book-skills/v06)<a href="https://agentmods.dev/skills/r2h1/my-book-skills/v06"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v06/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/r2h1/my-book-skills/v06"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v06.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00120 | $0.01842 |
| Opus 5 | $0.00060 | $0.00921 |
| Sonnet 5 | $0.00024 | $0.00368 |
| Haiku 4.5 | $0.00012 | $0.00184 |
Grade A, and why
付出者理论 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
付出者理论
R — 原文
"我立了一条铁律:白赚的钱,一定要拿出10%还给对方。"
— 金承焕, 第六章
I — 方法论骨架
付出者理论将人分为三类:
- 获取者(Taker) — 只接收不付出,拿完就走
- 互利者(Matcher) — 收到多少还多少,等价交换
- 付出者(Giver) — 倾囊相授,主动超额回报
核心发现:最穷的人是付出者,最有钱的人也是付出者。区别在于:
- 愚蠢的付出者 — 对获取者也付出,被利用后资源耗尽
- 聪明的付出者(Giver with strategy) — 战略性付出,在长期博弈中用'得一给二'建立信任溢价,好机会和人脉自动向自己聚集
作者的实操规则:
- 白赚的钱拿出10%还给对方——不是等价交换,而是超出对方预期
- 经常请客吃饭——用小钱测试自己是否有'用短期损失换长期回报'的判断力
- 吝啬是失败信号——在小钱上斤斤计较的人很难做出正确的长期决策
A1 — 书中的应用
案例 1: 作者报答炒股高手
- 问题: 炒股高手帮作者将20亿韩元增至30亿韩元且拒绝任何报酬
- 方法论的使用: 作者坚持给予回报——买了两辆车并承担江南新公寓月租,花费不到收益的10%
- 结论: 超额回报建立深层信任
- 结果: 高手深受感动,两人建立深度信任关系
案例 2: 分享股票信息转账1700万
- 问题: 朋友透露股票信息让作者赚了1.65亿韩元
- 方法论的使用: 作者当天转账1700万韩元(约10%)
- 结论: 其他人赚5000万只送2万礼物,作者的行为远超预期
- 结果: 朋友感动表示从未有人这样回报,日后更愿意优先分享机会
案例 3: 吝啬朋友的失败
- 问题: 吃完饭躲在收银台后面不想付10万韩元饭钱的朋友
- 方法论的使用: 从未使用——持续吝啬
- 结论: 反面案例——在小钱上计较的人失去人心和机会
- 结果: 作者从未见过吝啬的人中有人年纪轻轻就获得财富自由的
A2 — 触发场景
用户会在什么情境下需要这个 skill?
- 用户纠结'对方帮了我,我该怎么回报?'
- 用户不愿意请客/送礼/分享,觉得'凭什么'
- 用户想要建立人脉但不知道怎么开始
语言信号
- '我已经谢过他了,还要怎样?'
- '他那么有钱,不需要我回报'
- '凭什么要我请客?'
- '我给多了是不是吃亏?'
与相邻 skill 的区分
- 与
概率博弈决策框架的关系: 付出者理论是概率博弈在人际关系中的具体应用——'得一给二'是在长期重复博弈中的最优策略 - 与
五子棋理论的关系: 付出者理论是五子棋在人际关系中的棋步——'得一给二'是为未来连珠下的一步棋
E — 可执行步骤
当 skill 被激活后,agent 应按以下步骤执行:
-
识别当前关系中的博弈类型
- 帮助用户判断对方是Giver/Taker/Matcher
- 如果是Taker,建议设边界而非付出
- 如果是Giver或潜在Matcher,建议主动付出
- 完成标准: 用户明确了对方属于哪一类
-
计算'得一给二'方案
- 问:'对方帮了你多大忙?换算成价值是多少?'
- 建议回报价值 = 受益价值的10%-20%
- 回报形式: 现金 > 礼物 > 请客 > 口头感谢
- 完成标准: 用户确定了一个超出对方预期的具体回报方案
-
建立付出习惯
- 建议用户在下一次社交场合主动买单(不论金额大小)
- 培养'先付出后索取'的思维惯性
- 完成标准: 用户承诺在未来一周内执行一次主动付出
B — 边界
不要在以下情况使用此 skill
- 对方明显是Taker(只索取不回报)——付出给纯Taker等于资源浪费
- 用户处于被系统性剥削的关系/环境中
- 用户自身已经资源匮乏到无法付出
作者在书中警告的失败模式
- ce15: 吝啬者的失败——连饭钱都不愿请,失去人脉和机会
- ce16: 概率博弈中的情绪化决策——在回报时被'吃亏感'绑架
作者的盲点 / 时代局限
- 作者的'得一给二'建立在自身已较富裕的基础上,低收入者很难'拿出10%回报'
- 中国文化中的'人情世故'比韩国更复杂,'得一给二'在高度人情社会中可能被滥用的程度更高
- 作者没有讨论如何识别伪装的Taker(表面上像Giver实际上索取更多)
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.
- 11d ago First seen · 150 lines · 120 tokens per session scan A 9d7459735f76
付出者理论 is a skill published in the GitHub repository R2h1/my-book-skills (2 stars, last pushed 26d ago), licensed MIT. It adds 120 tokens to every session and 1,842 once invoked, about $0.0006 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.
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