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 peer-selectiongit 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/peer-selection)<a href="https://agentmods.dev/skills/kangarooking/cangjie-skill/peer-selection"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/peer-selection/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/peer-selection"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/peer-selection.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 | $0.00160 | $0.01616 |
| Opus 5.5 | $0.00064 | $0.00646 |
| Sonnet 5.5 | $0.00032 | $0.00323 |
| Haiku 4.5 | $0.00016 | $0.00162 |
Grade A, and why
peer-selection 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 1mo 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.
同伴选择：五只黑猩猩
R — 原文 (Reading)
有个“五只黑猩猩的理论”，你可以通过它最常接触的五个黑猩猩来预测一个黑猩猩的行为。……你不应该随意地选择你的朋友，仅仅是因为你们住的比较近或者碰巧在一起工作。选择了正确的五只黑猩猩的人，是最快乐和最乐观的人。
— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第二章·幸福
I — 方法论骨架 (Interpretation)
你的行为、情绪和成就由最常接触的五个人预测——所以同伴选择是人生最重要的主动决策之一。 三个原则： ① 主动选择——朋友不是地理/巧合的产物，而是价值观筛选的结果； ② 按场景配置——「工作时，身边要有比你更成功的人；玩乐时，身边要有比你更快乐的人」； ③ 远离负向者——愤世嫉俗者/悲观主义者会传染负面预期；处理冲突的第一规则是「不要和那些经常发生冲突的人在一起」；「如果你不能看到你与某人一起工作一辈子，那么连一天也不要和他共事」。 判断信号：总说自己诚实的人多半不诚实；花很多时间谈论价值观的人可能在掩盖什么； 价值观一致时小事不重要，价值观冲突才是争吵的根源。
A1 — 书中的应用 (Past Application)
案例 1: 贝赫扎德的「哇」
- 问题: 如何保持对生活的感恩与乐观
- 方法论的使用: 学习热爱生活、不浪费时间在不快乐的人身上
- 结论: 他的诀窍是「停止询问为什么，开始说哇」
- 结果: 成为作者心中选择正确同伴的样板
案例 2: 从生活中剔除负面者
- 问题: 有人做损害他人的事
- 方法论的使用: 第一次提醒，不改就保持距离、从生活中切割
- 结论: 「越想靠近我的，你的价值观必须要更好」
- 结果: 圈子成为价值观过滤后的结果
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 换城市/换工作后的交友
- 被朋友拖累：「朋友总在抱怨，我也变消极了」
- 择偶/亲密关系选择
- 想改变圈子：「我想认识更优秀/更快乐的人」
语言信号
- "我该交什么样的朋友"
- "要不要疏远某人"
- "怎么认识优秀的人/换圈子"
- "who should I surround myself with / toxic friends"
与相邻 skill 的区分
- 与
long-term-compounding的区别: 本 skill 选「和谁生活」；复利 skill 选「和谁做生意」 - 与
honesty-communication的区别: 诚实是自我标准，本 skill 是外部筛选标准
E — 可执行步骤 (Execution)
-
盘点你的五只黑猩猩
- 完成标准: 列出最常接触的 5 人，逐个标「积极/消极/中性」，评估他们预测了你的什么行为
-
按场景调整配置
- 完成标准: 明确工作圈和玩乐圈分别缺什么（更成功/更快乐），列出 1 个具体加入动作（活动/社群/项目）
-
处理负向关系
- 完成标准: 对每个消极关系选一：改造（明确沟通）、边界（减少接触频次）、或切割（退出）
- 判停条件: 若对方是亲属/同事无法切割，改为设置接触边界并补齐正向外圈
-
建立价值观检查
- 完成标准: 写下一份 3–5 条核心价值观清单，用来判断新朋友是否「小事不重要、大事一致」
B — 边界 (Boundary) ★
不要在以下情况使用此 skill
- 必须共事的同事/家人（先边界，再切割）
- 用户自身处于低谷、把责任全推给环境（先自我负责再选圈）
作者在书中警告的失败模式
- 随意选友: 「仅仅是因为你们住的比较近或者碰巧在一起工作」
- 与经常冲突的人在一起: 冲突是低质量关系的信号，不是性格磨合
作者的盲点 / 时代局限
- 「远离不快乐的人」在家庭/社群文化中的执行成本高，且可能滑向同温层
- 五只黑猩猩是预测性比喻，非严谨心理学结论
容易混淆的邻近方法论
long-term-compounding: 生意伙伴选「能共事一辈子」；生活同伴选「价值观一致+积极」
相关 skills (阶段 3 定稿)
- composes-with:
happiness-skill、long-term-compounding、honesty-communication
审计信息
- 验证通过: V1 ✓ / V2 ✓ / V3 ✓ (v15)
- 测试通过率: 见 test-results.md
- 蒸馏时间: 2026-08-01
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.
- 1mo ago First seen · 122 lines · 160 tokens per session scan A dcbbdc8a059a
peer-selection is a skill published in the GitHub repository kangarooking/cangjie-skill (11,633 stars, last pushed 22d ago), licensed MIT. It adds 160 tokens to every session and 1,616 once invoked, about $0.0006 per session on Opus 5.5. 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
An investing skill for regaining calm and making decisions based on a company’s long-term cash flows when price changes trigger fear, greed, or comparison with others.
good-business
An analysis framework for judging whether a company has a durable competitive advantage, meaning rivals may struggle to take its customers or profits over time.
opportunity-cost
An investment decision framework based on opportunity cost: what your money could do in another investment today. It treats keeping an asset as choosing to buy it again at its current price, regardless of your original purchase price.