huashu-skills is a catalogue of Agent Skills, which are reusable instruction packages that extend coding agents such as Claude Code, Codex, and Kimi Code for particular tasks. It organizes skills for design, research, document conversion, content creation, and other workflows, and includes machine-readable metadata and update checking. The catalogue entries are the skills and related tools that agents install and use.
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 alchaincyf/huashu-skills --skill huashu-speech-coachgit clone --depth 1 https://github.com/alchaincyf/huashu-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/alchaincyf/huashu-skills/huashu-speech-coach)<a href="https://agentmods.dev/skills/alchaincyf/huashu-skills/huashu-speech-coach"><img src="https://agentmods.dev/badge/skills/alchaincyf/huashu-skills/huashu-speech-coach/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/alchaincyf/huashu-skills/huashu-speech-coach"><img src="https://agentmods.dev/badge/skills/alchaincyf/huashu-skills/huashu-speech-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00102 | $0.03331 |
| Opus 5.5 | $0.00041 | $0.01332 |
| Sonnet 5.5 | $0.00020 | $0.00666 |
| Haiku 4.5 | $0.00010 | $0.00333 |
Grade A, and why
huashu-speech-coach 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
演讲教练 Presentation Coach
基于Patrick Winston教授（MIT）的How to Speak方法论 原始视频：https://youtu.be/Unzc731iCUY Winston教授2019年去世，这门课成为他的「最后一课」，在YouTube上收获1500万+观看 参考材料：
references/patrick-winston-how-to-speak.md
使用场景
| 场景 | 怎么帮 |
|---|---|
| 线下培训/工作坊 | 审查大纲结构、优化开场结尾、设计互动环节 |
| B站教程视频 | 优化脚本节奏、检查循环点、设计钩子 |
| 技术分享/meetup | 审查演讲稿、检查Winston规则、模拟观众视角 |
| PPT/Keynote审查 | 检查幻灯片「罪行」、优化信息密度 |
核心方法论
Winston的成功公式
演讲质量 = f(知识, 练习, 天赋)
天赋占比极小。真正决定演讲质量的是：你掌握多少关于演讲的知识（启发式规则），以及你练习了多少次。
一、如何开场
不要以笑话开场
观众刚到场时还在适应——收手机、调整坐姿、适应你的声音。这时讲笑话极容易冷场，而冷场会影响你整场的信心。
用「赋能承诺」开场（Empowerment Promise）
告诉观众：听完这段，你将获得什么。给他们一个留下来的理由。
好的承诺：
- 「接下来30分钟，你会学到3个可以今天就用的AI编程技巧」
- 「听完这段，你会知道为什么90%的人用AI工具的方式都是错的」
差的承诺：
- 「今天我来分享一下AI编程的经验」（太模糊，没有价值感）
- 「大家好，我是花叔，很高兴来到这里」（废话开场）
要求全注意力
人类只有一个语言处理器。如果观众在看手机，他们听不进去你说的话。更糟的是，看手机的人会分散旁边人的注意力，也会让演讲者分心。
线下培训时：直接要求合上电脑、放下手机 视频场景：前10秒必须给出足够强的理由让观众留下
二、演讲过程的四个核心技巧
1. 循环往复（Cycling）
在任何时刻，约20%的观众处于走神状态。通过3次重复核心概念（用不同方式），确保每个人都能接收到关键信息。
操作方法：「告诉他们你要讲什么 → 讲给他们听 → 告诉他们你讲了什么」
视频场景应用：每隔3-5分钟回扣主题，用一句话把观众拉回来。「所以回到我们的核心问题——为什么手搓经济突然火了？」
2. 筑墙划界（Build a Fence）
明确你的想法与他人想法的区别，防止观众混淆。
操作方法：说清楚「我讲的是什么」和「我讲的不是什么」
示例：
- 「我说的AI编程不是让AI帮你写Hello World，而是用AI从0到1做一个完整产品」
- 「这跟普通的Prompt Engineering不同，区别在于...」
3. 口头标点（Verbal Punctuation）
为走神的观众提供「重新上车」的机会。用明确的结构提示，让迷失的观众知道你讲到了哪里。
操作方法：
- 「好，第一点我们讲完了。现在进入第二点...」
- 「到这里我们已经讲了A和B，接下来是最关键的C...」
- 使用编号：「第一、第二、第三...」
4. 提问与等待（Ask Questions）
提问能瞬间抓回注意力。关键是提问后必须等待。
等待时间：至少7秒。不要因为沉默尴尬就自己回答。
问题难度：
- 太简单 → 观众觉得被侮辱
- 太难 → 没人敢回答
- 刚好 → 需要想一下但能答出来
视频场景：虽然观众无法真正回答，但提问制造了思考的停顿，比直接给答案更有效。
三、时间与地点
| 规则 | 要求 | 原因 |
|---|---|---|
| 最佳时间 | 上午11点 | 大家醒了，还没午饭犯困 |
| 灯光 | 全开 | 灯暗=睡觉信号。「闭着眼皮看不清幻灯片」 |
| 踩点 | 提前到场检查 | 了解设备、灯光开关、话筒、座位布局 |
| 填充房间 | 选会被填满一半以上的房间 | 人太少气氛冷清，宁可换小房间 |
四、工具与道具
黑板/白板的优势
- 图形化：方便即时画图解释
- 节奏控制：写字的速度正好匹配观众吸收速度（幻灯片往往太快）
- 手的归宿：给手找事做，避免插口袋或背后的尴尬姿态
- 共情镜像：观众会心理模拟你写字的动作，形成更深的参与感
What ships with it
1 file 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 · 304 lines · 102 tokens per session scan A 9ad3a3a81968
huashu-speech-coach is a skill published in the GitHub repository alchaincyf/huashu-skills (2,025 stars, last pushed yesterday), licensed MIT. It adds 102 tokens to every session and 3,331 once invoked, about $0.0004 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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