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 long-term-compoundinggit 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/long-term-compounding)<a href="https://agentmods.dev/skills/kangarooking/cangjie-skill/long-term-compounding"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/long-term-compounding.svg" alt="Measured on agentmods" 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 | $0.01609 |
| Opus 5 | $0.00068 | $0.00805 |
| Sonnet 5 | $0.00027 | $0.00322 |
| Haiku 4.5 | $0.00014 | $0.00161 |
Grade A, and why
long-term-compounding 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 8d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
长期复利游戏
R — 原文 (Reading)
玩复利游戏。无论是财富，人际关系或是知识，所有你人生里获得的回报，都来自于复利。……我的联合创始人Nivi说，“在一个长期游戏里，好像每个人都在让彼此发财，而在一个短期游戏里，好像每个人都在让自己发财。”
— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第一章·财富
I — 方法论骨架 (Interpretation)
把复利从金融概念升级为通用人生规律：财富、知识、声誉、关系都以指数方式积累， 所以选择的判据不是「现在能拿多少」，而是「这件事在十年尺度上是否复利」。 两个推论：① 只与「能想象共事一辈子」的长期伙伴合作——信任让谈判成本趋近于零； ② 只玩长期正和游戏——长期游戏里人人把饼做大，短期游戏里人人抢饼。 声誉是最典型的复利资产：持续几十年维护诚信，最终价值远超有才华但无声誉积累的人。 反面筛选信号：愤世嫉俗者、悲观主义者、短期思维者——他们会破坏复利结构。
A1 — 书中的应用 (Past Application)
案例 1: 与 Elad Gil 的交易
- 问题: 商业谈判成本高、信任难建立
- 方法论的使用: 与 Elad Gil 长期交易，对方主动多给好处、差额自掏腰包
- 结论: 信任让常规谈判极简，彼此愿意让利
- 结果: 作者几乎每笔交易都优先拉对方入局，关系进入复利循环
案例 2: 声誉换来别人做不了的交易
- 问题: 为什么巴菲特能买到别人买不到的公司
- 方法论的使用: 长期诚信+可靠+长期思维建立的声誉品牌
- 结论: 「你的性格和你的声誉是可以建立的……你知道这不是运气」
- 结果: 别人把「运气」当机会时，声誉者把机会变成确定收益
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 评估合作/合伙/签约对象：「这个人能合作五年十年吗」
- 犹豫是否接受短期高回报交易：「这单来钱快但伤口碑」
- 想积累声誉/复利资产：「怎么让机会主动来找我」
- 关系决策：「这段关系值得长期投入吗」
语言信号
- "长期 vs 短期怎么选"
- "这个人靠谱吗/值得长期合作吗"
- "怎么建立信任/声誉"
- "compounding / long-term game / is this person trustworthy"
与相邻 skill 的区分
- 与
game-selection的区别: 本 skill 关注时间尺度（长期/短期）；game-selection 关注博弈结构（零和/正和/单人） - 与
peer-selection的区别: 本 skill 的伙伴筛选服务于复利收益；peer-selection 服务于幸福与行为塑造
E — 可执行步骤 (Execution)
-
对候选机会跑复利测试
- 完成标准: 回答「十年后它还值多少？现在投入的信任/时间/钱会不会指数增长？」
- 判停条件: 若答案是「不可复利且只是快钱」，标记为短期游戏，慎重
-
对合作者跑『一辈子』测试
- 完成标准: 问「我能想象和这个人共事/生活一辈子吗？」；不能，则一天也别开始
-
检查负向信号
- 完成标准: 确认对方不是愤世嫉俗者/悲观主义者/短期思维者（他们要证明自己负面看法正确）
-
为声誉做一笔复利存款
- 完成标准: 本周期内做一件「对方会记得的好事」，不记账、不估量
B — 边界 (Boundary) ★
不要在以下情况使用此 skill
- 对方已在诈骗/违法边缘（先止损，不要用长期主义自我麻痹）
- 用户急需短期现金流救急（先解决生存，再谈复利）
作者在书中警告的失败模式
- 与愤世嫉俗者合作: 「他们会任由坏事发生，以证明他们负面看法是正确的」
- 估量付出: 「不要去估量——一旦开始计较，你的耐性就会耗尽」
作者的盲点 / 时代局限
- 长期游戏假设环境稳定可预期；在剧变行业/强监管环境，长期承诺也有风险
- 「所有好处都来自复利」是强断言，未考虑不可复利的必要止损
容易混淆的邻近方法论
game-selection: 先识别博弈结构，再决定玩长期还是短期
相关 skills (阶段 3 定稿)
- composes-with:
wealth-structure（复利结构）、peer-selection（长期伙伴） - contrasts-with:
game-selection（时间尺度 vs 博弈结构）
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.
- 8d ago First seen · 122 lines · 136 tokens per session scan A cfae5df1c8c7
long-term-compounding is a skill published in the GitHub repository kangarooking/cangjie-skill (9,662 stars, last pushed 2d ago), licensed MIT. It adds 136 tokens to every session and 1,609 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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equanimity-mindset
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good-business
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