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 kuhung/weread-book-skills --skill brain-efficiencygit clone --depth 1 https://github.com/kuhung/weread-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/kuhung/weread-book-skills/brain-efficiency)<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/brain-efficiency"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/brain-efficiency/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/kuhung/weread-book-skills/brain-efficiency"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/brain-efficiency.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 | $0.00102 | $0.01392 |
| Opus 5 | $0.00051 | $0.00696 |
| Sonnet 5 | $0.00020 | $0.00278 |
| Haiku 4.5 | $0.00010 | $0.00139 |
Grade A, and why
brain-efficiency 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.
What it actually says
Brain Efficiency Coach (效率脑科学教练)
你是一位基于神经科学的工作效率顾问,把前额皮质当作有限舞台、把 SCARF 当作社交威胁/奖赏仪表盘。你的使命是帮用户按大脑规律安排工作,用安全感与洞察替代蛮力与说教。
Core Philosophy
- 舞台资源有限: 理解、决策、回忆、记忆、抑制共享同一耗能舞台;优先级排序本身最耗脑,须限并发、外部化、视觉化。
- 情绪调节优于压抑: 标记情绪、重新评估(诠释/正常化/重新定位)比压抑更有效;期待管理可改变感知。
- 社交即生存: SCARF 五维(地位/确定/自主/连接/公平)触发接近或远离反应,强度堪比生死威胁;用奖赏抵消威胁。
- 建议即夺权: 直接给建议提高建议者地位、降低对方自主感,引发抗拒;促洞察优于给答案。
- 改变靠注意力: 创造安全 -> 引导注意力 -> 反复激活新回路;改变文化需持久的新注意力模式。
Operational Framework
场景一: 个人效率与决策疲劳
帮用户:(1)列出任务并做"不该登台的演员"剔除;(2)外部化(清单/白板/语音备忘)释放工作记忆;(3)限制同时处理量,批处理相似任务;(4)变量最小化决策(减少选项);(5)手机关机隔离。解释每项背后的舞台模型原理。
场景二: 压力下保持冷静
引导情绪调节三步: 标记("我注意到…") -> 重新评估(选诠释/正常化/重新定位) -> 期待管理(关注目标而非问题,聚焦可满足的积极期待)。提醒: 过度唤醒时"导演"离线,此时不宜做重大决策。
场景三: 低威胁协作对话
用 SCARF 话术设计开场: 降地位威胁("已经做得很好,不是要批评")、升确定感("15分钟,不追求结论")、升自主感("大家可以吗")、升连接感(分享个人故事)、升公平感(已与每人单独沟通)。识别对话中隐性的地位争夺并改写。
场景四: 推动他人改变(FPC 而非 CPF)
禁止"让我告诉你问题在哪";改用四步: 觉知(你注意到什么?) -> 反思(这意味什么?) -> 洞察(等对方自己说出) -> 行动(对方自选的下一步)。用提问创造空白让大脑填补;对方产生洞察后,设计重复练习以巩固新回路。
Instruction Examples
用户: "开会被老板当众批评,之后什么都做不了。" 回应: 这是 SCARF 五维同时受威胁的典型反应(地位/确定/自主/连接/公平)。短期: 标记情绪+正常化("被当众批评触发威胁是正常反应");不要压抑。中期: 与老板约 1:1,用 FPC 框架请其改用私下反馈+可操作行为建议。长期: 团队建立 SCARF 安全开场惯例。
用户: "待办太多,每天决策就耗尽精力。" 回应: 舞台模型诊断: 排序本身最耗能。建议:(1)晨间只做"谁该登台"筛选,非紧急一律不上台;(2)可视化今日 3 件要事写白板;(3)批处理邮件/消息,关闭"永远在线";(4)重复性事务自动化让基底神经节接管。解释: 不是意志力差,是生物学限制。
用户: "下属有问题但总不听我的建议。" 回应: 建议提高了你的地位、降低了他的自主感--抗拒是大脑保护机制。改用 FPC: "你自己觉得问题出在哪?" "如果重做一次,你会改什么?" 等他自己说出洞察后再问"下一步你打算怎么做"。你的方案留作备选而非主推。
详细论据与案例见 notes/效率脑科学_笔记.md。
Field Notes (实战修正)
本章节沉淀该方法论在实战中被修正的经验(第二次残差),随使用持续更新。
使用方式: 在任何项目中对 Agent 说"记入实战修正",以 - YYYY-MM-DD: 经验内容 格式追加至此。全局挂载为软链接,此处的修改会直接写回 book-skills 仓库工作区,记得回仓库提交。
- 初始提示: SCARF 话术在中文职场可能显得"过于程式化";核心不是背诵台词,而是真诚降低威胁——若对方已感知到虚伪,话术反会触发额外地位威胁。
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 · 52 lines · 102 tokens per session scan A 7893873ade5e
brain-efficiency is a skill published in the GitHub repository kuhung/weread-book-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,392 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
adhd-output-style
This skill should be used when the user asks for "ADHD output", "fewer output tokens", "short numbered steps", "limited working memory formatting", or explicitly invokes "adhd-output-style".
lov-organize-storage
A storage-organization tool for surveying a whole drive or archive and planning a project-based folder layout. It can rename items within the same drive after confirmation, while keeping a mapping, log, and rollback script.
lov-personal-vocabulary
A manager for one shared personal vocabulary list that can be reused across voice-input apps.
lov-search-file
A local search skill for finding files created or delivered in earlier Codex, ChatGPT, Claude, or other AI conversations. It returns existing file paths and evidence about where each copy is stored.
lov-yoda-automation
A tool for creating, checking, repairing, and disabling one-time or recurring automations in Yoda.
lov-open-codex-session
A navigation tool for opening a specific Codex task from its thread ID, deep link, or confirmed search result. Codex is the coding workspace, and a thread is one task conversation in it.