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 screen-detoxgit 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/screen-detox)<a href="https://agentmods.dev/skills/kangarooking/cangjie-skill/screen-detox"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/screen-detox/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/screen-detox"><img src="https://agentmods.dev/badge/skills/kangarooking/cangjie-skill/screen-detox.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 | $0.00148 | $0.01664 |
| Opus 5.5 | $0.00059 | $0.00666 |
| Sonnet 5.5 | $0.00030 | $0.00333 |
| Haiku 4.5 | $0.00015 | $0.00166 |
Grade A, and why
screen-detox 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
屏幕戒断与多巴胺管理
R — 原文 (Reading)
没有例外——所有屏幕活动都与更少幸福有关，所有非屏幕的活动都与更多的幸福联系在一起。……我只是整日吃着小多巴胺的零食。我得到了我小140字的多巴胺。我发推特，然后留意谁转推了我的推文。
— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第二章·幸福
I — 方法论骨架 (Interpretation)
屏幕内容（社交媒体/新闻/短视频/游戏）是精心设计的多巴胺零食： 用即时奖赏的长期后果（注意力涣散、幸福下降、焦虑升高）换短期快感。 戒断的逻辑不是靠意志力硬扛，而是： ① 承认机制——屏幕=用长期后果换短期利益（咖啡因同理），「所有屏幕活动都与更少幸福相关」； ② 识别替代——阳光、运动、积极思考、面对面交流是天然血清素来源，非屏幕活动与更多幸福相关； ③ 习惯替换五步——挑一件事→培养一个愿望→可视化→规划可持续道路→识别需求/触发/替代品→告诉朋友→仔细调查； ④ 环境设计——少用电话/日历/闹钟（三个让你重新回到数字世界循环的 app）。 现代斗争的真相：孤独的个体用非人的意志力，对抗大量科学家和统计学家设计的「垃圾食品、点击诱导新闻、无限色情、无尽游戏和上瘾药物」。
A1 — 书中的应用 (Past Application)
案例 1: 从 Twitter 回到书籍
- 问题: 沉迷推文多巴胺，意识到「我其实学不到任何东西」
- 方法论的使用: 承认这是游戏，重新把书当博客读（无读完义务）
- 结论: 「我意识到我必须回去读书」
- 结果: 阅读重新回到他的生活
案例 2: 丢掉咖啡因与每天锻炼
- 问题: 情绪不稳、幸福基线低
- 方法论的使用: 戒咖啡因、每天锻炼、晒太阳、抬头微笑
- 结论: 「我认为丢掉咖啡因使我更快乐。这使我变得更加稳重。」
- 结果: 这些成为第二天性的幸福习惯
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 刷手机失控：「一刷就两小时，停不下来」
- 空虚感：「刷完更空虚/焦虑」
- 想戒某个坏习惯：「怎么戒短视频/游戏/零食」
- 想提升专注与幸福：「把时间还给自己」
语言信号
- "我刷手机上瘾怎么办"
- "怎么戒掉短视频/游戏/熬夜刷手机"
- "感觉被算法控制"
- "screen addiction / doomscrolling / dopamine detox"
与相邻 skill 的区分
- 与
reading-metaskill的区别: 阅读是替代屏幕的深度输入，但戒断本身是本 skill 职责 - 与
monkey-mind-meditation的区别: 本 skill 管外部刺激源，冥想管内部杂念
E — 可执行步骤 (Execution)
-
量化当前屏幕消耗
- 完成标准: 用屏幕时间统计记录 3 天，标出最痛的 1 个消费型 app/时段
-
做一个环境改造
- 完成标准: 把该 app 移出首屏/卸载/设限额，物理隔离（手机放另一房间）
-
设计替代活动
- 完成标准: 为每个触发时段（通勤/睡前/等饭）准备一个非屏幕替代（书/散步/面对面聊天）
-
对外承诺
- 完成标准: 告诉 3 个朋友你的戒断目标，接受他们的监督
- 判停条件: 若 7 天后复发 ≥3 次，缩小目标（如从「戒全部」改为「只戒睡前 1 小时」），不责备自己
B — 边界 (Boundary) ★
不要在以下情况使用此 skill
- 用户靠屏幕工作谋生（区分工作屏幕与消费屏幕，别一刀切）
- 用屏幕维持社交联系的孤独老人/异地关系（先保留必要联系）
作者在书中警告的失败模式
- 用意志力对抗设计好的武器: 「孤独的个体召唤非人的意志力……去对抗大量的科学家和统计学家」
- 以为「看新闻=学习」: 新闻的工作是让你焦虑和生气
作者的盲点 / 时代局限
- 「所有屏幕活动与更少幸福相关」是强断言，未区分社交连接型屏幕使用
- 对必须用屏幕的现代职业缺乏解决方案
容易混淆的邻近方法论
reading-metaskill: 两者互补：戒屏幕→把时间给阅读
相关 skills (阶段 3 定稿)
- composes-with:
happiness-skill、monkey-mind-meditation - contrasts-with:
reading-metaskill
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 · 124 lines · 148 tokens per session scan A 9868194be4ae
screen-detox is a skill published in the GitHub repository kangarooking/cangjie-skill (11,353 stars, last pushed 14d ago), licensed MIT. It adds 148 tokens to every session and 1,664 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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