Cangjie Skill is a system that turns methods from books, long videos, podcasts, and other source material into executable skills for AI agents. It helps users package knowledge into callable workflows, using the repository's code, methods, and templates; catalogue add-ons relate to these agent skills.
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 kangarooking/cangjie-skill --skill decision-heuristicsgit clone --depth 1 https://github.com/kangarooking/cangjie-skillWrote 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/kangarooking/cangjie-skill/decision-heuristics)<a href="https://agentmods.dev/skills/kangarooking/cangjie-skill/decision-heuristics"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/decision-heuristics/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/kangarooking/cangjie-skill/decision-heuristics"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/decision-heuristics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00136 | $0.01616 |
| Opus 5 | $0.00068 | $0.00808 |
| Sonnet 5 | $0.00027 | $0.00323 |
| Haiku 4.5 | $0.00014 | $0.00162 |
Grade A, and why
decision-heuristics 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
重大决策启发式
R — 原文 (Reading)
如果你无法下决定，就回答否。原因是，现代社会充满了选择。……当你选择某些事物时，你会被锁定一段很长的时间。……简单的启发式算法：如果你在一个困难的决定上有同等程度的分歧，那么就选择在短期内会更痛苦的那条路。
— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第一章·财富
I — 方法论骨架 (Interpretation)
现代社会的选择无穷多，而每个重大选择的锁定时间极长（创业十年、感情五年以上、城市十年以上）， 所以决策的默认值是「否」——只有非常确定时才说「是」。 三条可执行启发式： ① 决策门：无法决定就答否；做利弊电子表格本身就是「不够确定」的信号，忘掉它； ② 三个重大决定：住哪/和谁在一起/做什么决定人生轨迹，值得花一两年认真决定，同时对所有不重要的事说不； ③ 短期痛苦原则：两个均等选项之间，选短期更痛苦的那条——按复利逻辑它对应长期收益，而大脑本能回避短期痛苦。 这套启发式专治「过度纠结」，不是用来分析日常小选择。
A1 — 书中的应用 (Past Application)
案例 1: 读不懂的书与练酸痛的肌肉
- 问题: 大脑回避短期痛苦（读难书/健身）
- 方法论的使用: 把短期痛苦当长期收益的信号
- 结论: 「生命中大多数收益都是来自于短期的痛苦」
- 结果: 作者用健身/读书验证该原则并长期执行
案例 2: 冷水澡的启示
- 问题: 每天早上面对令人逃避的冷水澡
- 方法论的使用: 大部分痛苦来自逃避，直接走进去
- 结论: 「一旦你进去了，你就是进去了。这不是痛苦。这只是冷。」
- 结果: 成为每天重训该教训的习惯
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 重大选择纠结：「要不要跳槽/买房/搬去另一个城市/合伙」
- 利弊表做了很久：「列了十项利弊还是拿不定主意」
- 机会太多：「这个也想做那个也想做，怎么选」
- 需要说不：「怎么拒绝一个'还行'的机会」
语言信号
- "我纠结要不要……"
- "这个决定该不该做"
- "我该选哪个（两个选项都还行）"
- "can't decide / should I take it / pros and cons"
与相邻 skill 的区分
- 与
acceptance的区别: 本 skill 处理「能改变的选择」；接受处理「不能改变/只能接受或离开」的处境 - 与
judgment-training的区别: 判断力是底层能力，本 skill 是即用启发式
E — 可执行步骤 (Execution)
-
判断这是不是重大决定
- 完成标准: 是「住哪/和谁/做什么」类（锁定 ≥5 年）才算；日常选择走普通决策
-
跑决策门
- 完成标准: 若无法当场决定且开始列利弊表 → 默认答「否」，除非有非常确定的新证据
-
两个均等选项时选短期更痛苦的路
- 完成标准: 明确两个选项「均等」，确认其中一个短期更痛苦，选它
- 判停条件: 若选项不等（一方明显更差），直接排除差的，不套启发式
-
给重大决定分配应有的时间
- 完成标准: 对影响十年生活的决定，规划 1–2 年的调研窗口，期间对不重要的机会说不
B — 边界 (Boundary) ★
不要在以下情况使用此 skill
- 日常琐碎选择（晚餐/穿搭/娱乐）——启发式会误伤生活
- 对方已有明确偏好、只需要执行（先确认真实犹豫点）
作者在书中警告的失败模式
- 用列表逃避决策: 「如果你发现你自己做了一个关于决定的电子表格……那就忘了它吧」
- 对不重要的事说「是」: 会把时间全部占满，无法聚焦重大问题
作者的盲点 / 时代局限
- 「答否」默认值适合机会富余者；资源稀缺环境里「否」可能让你错过唯一机会
- 短期痛苦≠总是正确（存在纯粹的坏痛苦），需先排除「纯损害」选项
容易混淆的邻近方法论
hourly-rate-time: 时薪解决时间价值，本 skill 解决选择结构
相关 skills (阶段 3 定稿)
- composes-with:
judgment-training（启发式背后的能力）、hourly-rate-time（时薪作量化判据） - contrasts-with:
acceptance（能改变 vs 只能接受）
What ships with it
2 files 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.
- 11d ago First seen · 123 lines · 136 tokens per session scan A fe7fd497e890
decision-heuristics is a skill published in the GitHub repository kangarooking/cangjie-skill (9,795 stars, last pushed 4d ago), licensed MIT. It adds 136 tokens to every session and 1,616 once invoked, about $0.0007 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 2026-08-30.
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dcf-valuation
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ownership-mindset
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