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 v14-vsgit 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/v14-vs)<a href="https://agentmods.dev/skills/r2h1/my-book-skills/v14-vs"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v14-vs/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/v14-vs"><img src="https://agentmods.dev/badge/skills/r2h1/my-book-skills/v14-vs.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 | $0.00104 | $0.02005 |
| Opus 5 | $0.00052 | $0.01002 |
| Sonnet 5 | $0.00021 | $0.00401 |
| Haiku 4.5 | $0.00010 | $0.00200 |
Grade A, and why
interest-vs-commitment-redline 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.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
兴趣 vs 承诺：红线原则
R — 原文 (Reading)
读一本书是兴趣，而经常运用书中学到的知识则是承担责任。兴趣是想开始一项业务，而承担责任是完成有限公司的注册文档。从周一到周五每天工作1小时是兴趣，而每周工作7天，只要有时间就工作则是承担责任。租一辆昂贵的轿车是兴趣，骑着自行车为经营系统而奔走则是承担责任。兴趣让你看上去富有，承担责任使你变得富有。
— MJ·德马科, 第28章
很多人一生都在逍遥地用第一挡行进，却在问自己为何是在这里？人人都对金钱感兴趣，不幸的是，仅对财富和财务安全"感兴趣"并没有什么用，他们依旧在用第一挡行驶。为了超越第一挡，你必须做出相应的努力和一系列正确选择，充分利用快车道的力量。在兴趣和承担责任之间，存在着本质的区别。
— MJ·德马科, 第28章
I — 方法论骨架 (Interpretation)
兴趣和承诺之间有一道不可跨越的红线。兴趣是消费行为——你读书、听课、调研、讨论，这些让你感觉自己在前进，实际上你只是在"输入"。承诺是生产行为——你注册公司、上线产品、签下第一个客户、承受第一次亏损，这些让你真正在市场中被检验。
红线原则的核心判断标准只有一条：你的行为是否产生了可被市场验证的输出？ 如果你的努力无法被外部市场"投票"（买单或拒绝），你就在兴趣区；如果市场已经可以对你的输出说"是"或"不"，你就在承诺区。
区分二者的具体标尺：
- 时间投入的性质：兴趣花时间在"准备准备"，承诺花时间在"交付交付"。
- 对失败的承受：兴趣遇到困难退回学习状态，承诺遇到困难调整方案继续推进。
- 对外表达的方式：兴趣说"我想/我打算/我在研究"，承诺说"我做了/我发布了/我签了"。
A1 — 书中的应用 (Past Application)
案例：德马科本人创业经历
德马科大学毕业后没有找工作，直接创业。家人认为他疯了，同龄人以为他妄想。他靠送比萨和开豪华轿车维生，住在狭窄公寓里，午餐晚餐吃廉价意大利面条。他放弃了晚上与朋友饮酒欢聚，连续数周每天工作12小时建立和完善系统。当他朋友沉迷于电子游戏赛车时，他在构建真实业务。
这就是红线行为：他做了别人不愿意做的事，承受了别人不愿意承受的社交压力和经济拮据。兴趣会让人在第一次失败后说"这行不通"，承诺让他在失败100次后还在坚持。
案例：托马斯·爱迪生 vs 普通发明爱好者
德马科引用爱迪生的例子：如果仅凭兴趣，经历3次失败就放弃了；而承诺是直到失败100次之后还在坚持。爱迪生不是因为"对灯泡感兴趣"而发明了灯泡，而是因为他对"让世界通电"做出了承诺。
A2 — 触发场景 (Future Trigger) ★
典型触发场景：
- 你发现自己连续几周/几个月在"学习"创业知识但没有任何对外交付
- 你在和人交谈时说"我打算做……"但说不出具体的启动日期和第一个里程碑
- 你遇到困难后的第一反应是再报一个课、再读一本书、再做一次调研
- 你发现自己在美化"准备阶段"，用"我在深入研究"来回避"我不敢开始"
语言信号：
- "等我把这个课程学完就开始"
- "等我找到完美的合伙人"
- "等我存够钱"
- "等我更有经验"
与相邻技能区分：
- 与 v15（执行vs点子）的区别：红线原则判断的是"你此刻在兴趣区还是承诺区"；执行vs点子回答的是"我的点子够不够好到可以行动"——前者是心态定位，后者是行动策略。
- 与 v16（明智风险）的区别：红线原则推动你跨过"不敢行动"的门槛；明智风险帮你判断"跨过门槛后该冒哪些险、避哪些险"。
E — 可执行步骤 (Execution)
当技能被激活后，按以下步骤执行：
-
自检红线位置：写下过去30天你在该目标上花的时间，分类为"输入型时间"（读书/听课/调研/讨论）和"输出型时间"（生产/发布/销售/交付）。如果输入型占比超过80%，你就在兴趣区。
-
设定红线指标：为自己定义一个不可撤销的"红线日期"和"红线产出"——例如"下周五之前必须上线一个最小可用版本"或"本周结束前必须找到第一个付费客户"。这个指标必须可被外部验证。
-
移除安全网：红线原则要求你把自己置于"不得不做"的位置。公开承诺、向朋友宣布截止日期、投入不可退回的资源（如预付域名/服务器费用）。消除"我可以再准备一下"的后路。
-
执行最小交付：不要追求完美。承诺的第一个动作永远是"交付一个不完美但完整的东西"。一个简陋的网站、一个手写的方案、一个当面推销——市场反馈比任何书本都真实。
-
遭遇困难时用"调整"代替"退回"：如果红线指标失败，不要退回学习模式。分析失败原因，调整方案，设定新的红线日期，继续输出。承诺的标志是：失败后下一个动作仍然是输出，不是输入。
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 · 97 lines · 104 tokens per session scan A f83596d0e95f
interest-vs-commitment-redline is a skill published in the GitHub repository R2h1/my-book-skills (2 stars, last pushed 27d ago), licensed MIT. It adds 104 tokens to every session and 2,005 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.
Other skills, from other repositories
shortfilm-prompt
Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell…
multi-agent-cn
A Chinese-language system design that makes one main agent assign tasks to five persistent subagents instead of doing the work itself.
gpt-image-2-prompt-engine
A structured prompt-writing tool for GPT-Image-2, using reusable JSON templates and a four-step workflow. It is designed for controlled commercial images such as product photos, posters, infographics, brand visuals, UI screenshots, and character designs.
simp
A relationship-advice skill that helps interpret signals, plan respectful approaches, and write sincere messages for someone you like.
chinese-write-checker
A Chinese-language review process for checking an article from an editor's, reader's, writer's, and publishing platform's viewpoints.
editor-revisor
A Chinese-language editing assistant that finds unnecessary words, formulaic openings, forced parallel phrasing, and slogan-like endings. It offers a deletion-focused mode and a fuller rewriting mode for any draft.