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 v03-72git 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/v03-72)<a href="https://agentmods.dev/skills/r2h1/my-book-skills/v03-72"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v03-72/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/v03-72"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v03-72.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 | $0.00041 | $0.00716 |
| Opus 5 | $0.00020 | $0.00358 |
| Sonnet 5 | $0.00008 | $0.00143 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
v03-72小时行动法则 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
72小时行动法则
R（核心引文）
"当你决定做一件事情的时候，你必须在 72 小时之内完成它，否则你很可能就永远不会再做了。"
"不是试试看，而是去行动 ! 如果你只是带着试试看的心态，那么你最后只会以失败而告终，你会一事无成。尝试纯粹是一种借口，你还没有做，就已经给自己想好了退路。不能试验，你只有两个选择——做或者不做。"
I（底层原理）
72小时是行动窗口期。决策后的兴奋和动力在72小时内保持高峰，之后迅速衰减。拖延的本质是大脑对新行动的心理门槛随时间升高。72小时内完成第一步（哪怕是很小的一步）能绕过这个门槛，让行动进入正反馈循环。
A1（过去应用）
吉娅决定做梦想相册后因为拖延一直没找照片，钱钱指出她违反了72小时规定。之后她立即行动：当天准备好储蓄罐，第二天开始收集照片。同样，她决定演讲后72小时内完成了报名和排练安排。
A2（未来触发场景）
- 你刚做了一个决定但感觉"等准备好了再做"时
- 一个新想法出现但兴奋感在下降时
- 任何你想做但一直在拖延的事情
- 学了新技能但还没实践时
E（执行步骤）
- 做决定时立即设定72小时内的截止时间
- 写下来并告诉一个人你的计划（社会承诺效应）
- 72小时内完成第一步（不要求全部完成，哪怕是打个电话、写个开头）
- 完成后给自己一个奖励
- 如果错过72小时，重新做决定而不是放弃
B（边界与局限）
重大决策需要充分调研，72小时内强行行动可能会仓促犯错。区分"谨慎决策"和"拖延借口"是关键。此法适用于执行类决定而非决策类决定（先决策再执行，决策环节可以慢）。
相关技能
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 · 51 lines · 41 tokens per session scan A 8ddd4d8ef214
v03-72小时行动法则 is a skill published in the GitHub repository R2h1/my-book-skills (2 stars, last pushed 27d ago), licensed MIT. It adds 41 tokens to every session and 716 once invoked, about $0.0002 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.
Other skills, from other repositories
accint-commitments
Triage acc's open promises and close them with honest real-world verdicts via accact(runtime="outcome").
ai-efficiency-audit
Audit a person's or team's weekly workflow, identify which tasks AI can realistically take over, and produce an evidence-based, manager-ready efficiency proposal with hours-saved ranges and a two-week pilot plan. Use this skill whenever the user mentions AI efficiency pressure from leadership, or says any of these …
weekly-report-alchemy
A tool that turns scattered work records—such as chat logs, code commits, calendar entries, and notes—into a structured weekly or monthly report.
screen-detox
A habit-change framework for reducing non-work screen use such as social media, short videos, games, and endless scrolling.
monkey-mind-meditation
A meditation and reflection guide for a busy or anxious mind. It treats recurring thoughts as something to observe and examine, while noting that acute serious mental-health problems need professional care.
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so Claude can answer questions on your team's behalf. Team-scope sibling to second-brain (which is personal-scope). Seven modes — capture (drop something into the right structured dir), compile (process into wiki…