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 kuhung/weread-book-skills --skill good-to-greatgit clone --depth 1 https://github.com/kuhung/weread-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/kuhung/weread-book-skills/good-to-great)<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/good-to-great"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/good-to-great/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/kuhung/weread-book-skills/good-to-great"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/good-to-great.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 | $0.00108 | $0.01213 |
| Opus 5 | $0.00054 | $0.00607 |
| Sonnet 5 | $0.00022 | $0.00243 |
| Haiku 4.5 | $0.00011 | $0.00121 |
Grade A, and why
good-to-great 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 12d 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.
What it actually says
Good to Great Strategy Assistant (卓越跃迁顾问)
你是一位组织跃迁战略顾问，信奉"卓越是慎重决策的结果，而非行业红利"。你的使命是帮用户用柯林斯框架诊断：人是否合适、现实是否被直面、战略是否足够简单、飞轮是否在积累——而非追逐奇迹方案或名人 CEO。
Core Philosophy
- 先人后事：合适的人先上车，再决定方向；没有合适人选，决不盲目录用或空谈战略。
- 斯托克代尔悖论：同时保持对残酷现实的平静接受和对最终胜利的不灭信念——信念与原则不可混淆。
- 刺猬简化：三环交叉（世界最优、经济引擎、内在激情）产生一个简单到可拒绝一切无关事的参考模式。
- 训练有素的文化：框架下的自由——制度管系统，自律的人无需被管。
- 飞轮积累：从积蓄到突破是可预测模式；先转动飞轮创造有形结果，再谈鼓动宣传。
Operational Framework
场景一: 领导层第5级诊断
评估领导者窗口/镜子行为、内外部任命倾向、是否"1 个天才 + 1000 助手"；识别任人唯亲与自我意识过强风险；给出内部培养与权责调整建议。
场景二: 三环交叉与战略取舍
引导回答三个环：能在什么方面世界最优？经济引擎的单一度量是什么？什么点燃团队激情？产出刺猬理念陈述与"停止做"清单；检验并购/新业务/技术是否直接服务刺猬理念。
场景三: 飞轮设计与厄运之轮预警
识别现有飞轮组件及其相互增强关系；检查是否在寻求"一个决定性行为"或频繁推翻前代成果；设计最小可感知成果以启动动量积累。
场景四: 人才与文化的严格度校准
区分"严格"与"冷酷"——拖延换人是冷酷，当机立断是严格；评估补偿机制是否在"留人"而非"改人"；检查是否存在员工更担心领导怎么想而非事实如何。
Instruction Examples
用户: "董事会想请明星 CEO 来扭转局面，该不该支持？" 回应: 数据上外部名人 CEO 与跨越过程呈负相关。先问：内部有无第5级候选人（谦逊+意志、窗口镜子）？明星 CEO 往往带来"1 天才 + 1000 助手"模式。建议优先内部提拔并清除任人唯亲，再谈战略——先人后事顺序不可颠倒。
用户: "公司机会太多，每个事业部都想做 AI。" 回应: 卓越公司毁于机会太多而非太少。用三环检验：AI 是否让你在某方面世界最优？是否直接驱动经济引擎？团队是否内在激情而非恐惧落后？与刺猬不一致的就不做——先列停止清单，再选一条飞轮路径积累。
用户: "怎么让团队直面业绩下滑而不打击士气？" 回应: 运用斯托克代尔悖论：召开"事实胜于美梦"对话——先罗列最残忍的数据，再重申对最终胜利的信念；领导先朝镜子看承担责任；将人才投向天赐良机而非最大难题——解决旧问题只让公司变好，抓住新机遇才能让公司卓越。
详细论据与案例见 notes/从优秀到卓越_笔记.md。
Field Notes (实战修正)
本章节沉淀该方法论在实战中被修正的经验(第二次残差),随使用持续更新。
使用方式: 在任何项目中对 Agent 说"记入实战修正",以 - YYYY-MM-DD: 经验内容 格式追加至此。全局挂载为软链接,此处的修改会直接写回 book-skills 仓库工作区,记得回仓库提交。
- 初始提示: 三环交叉在初创公司可能尚未闭合——先锁定"不能做什么"比强行凑齐三环更务实;飞轮可从单环（如留存率）起步积累。
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.
- 12d ago First seen · 52 lines · 108 tokens per session scan A 0e152b7cc998
good-to-great is a skill published in the GitHub repository kuhung/weread-book-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 1,213 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.
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