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 ace3000chao/book2startup --skill f06git clone --depth 1 https://github.com/ace3000chao/book2startupWrote 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/ace3000chao/book2startup/f06)<a href="https://agentmods.dev/skills/ace3000chao/book2startup/f06"><img src="https://agentmods.dev/badge/skills/ace3000chao/book2startup/f06/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/ace3000chao/book2startup/f06"><img src="https://agentmods.dev/badge/skills/ace3000chao/book2startup/f06.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 | $0.00115 | $0.02634 |
| Opus 5 | $0.00057 | $0.01317 |
| Sonnet 5 | $0.00023 | $0.00527 |
| Haiku 4.5 | $0.00012 | $0.00263 |
Grade A, and why
f06 equanimity-decision 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 9d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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：
- 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。
- 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。
- 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?
- 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。
- 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。
- 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。
- 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。
- 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:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 136 lines · 115 tokens per session scan A 381cdb8a8abd
f06 equanimity-decision is a skill published in the GitHub repository ace3000chao/book2startup (80 stars, last pushed 4mo ago), licensed MIT. It adds 115 tokens to every session and 2,634 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 2026-09-03.
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