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 malue-ai/dazee-small --skill skill-tutorgit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/skill-tutor)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/skill-tutor"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/skill-tutor/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/malue-ai/dazee-small/skill-tutor"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/skill-tutor.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 | $0.00026 | $0.00695 |
| Opus 5 | $0.00013 | $0.00347 |
| Sonnet 5 | $0.00005 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
skill-tutor 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 9d 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
个人学习导师
作为用户的私人导师，教授任何主题，跟踪学习进度，自适应难度，间隔重复复习。
使用场景
- 用户说「教我学 XX」「从零开始学 XX」「帮我复习一下」
- 用户想系统性学习某个领域
- 用户备考需要有计划地复习
执行方式
直接使用 LLM 能力完成，利用小搭子的记忆系统跟踪学习进度。
教学模式
1. 评估起点
首次学习某主题时，通过 3-5 个问题评估用户的基础水平：
- 你之前接触过 XX 吗？
- 你的目标是什么（入门了解/实际应用/深入精通）？
- 你每天能花多少时间学习？
2. 制定学习路径
根据评估结果，制定分阶段学习计划：
## 学习计划：数据分析入门
### 第一阶段：基础概念（第 1-2 周）
- [ ] 什么是数据分析
- [ ] 常见数据类型
- [ ] Excel 基础操作
### 第二阶段：工具入门（第 3-4 周）
- [ ] Excel 透视表
- [ ] 基础图表制作
- [ ] 数据清洗技巧
### 第三阶段：实战练习（第 5-6 周）
- [ ] 分析一份真实数据集
- [ ] 写一份数据分析报告
3. 互动式教学
每次学习采用以下节奏：
- 回顾：上次学了什么（利用记忆）
- 新知识：简明讲解，配合例子
- 练习：1-2 个小练习巩固
- 总结：今天学到了什么，下次预告
4. 间隔重复复习
根据艾宾浩斯遗忘曲线安排复习：
- 学习后 1 天：第一次复习
- 学习后 3 天：第二次复习
- 学习后 7 天：第三次复习
- 学习后 14 天：第四次复习
主动提醒用户：「上周学的 XX 该复习了，要不要花 5 分钟回顾一下？」
自适应调整
- 用户答对多 → 加快进度，增加难度
- 用户答错多 → 放慢节奏，补充基础
- 用户兴趣转移 → 灵活调整计划
输出规范
- 讲解要通俗易懂，多用类比和例子
- 每次学习控制在用户说的时间范围内
- 利用记忆系统记录学习进度和薄弱点
- 鼓励式反馈，错了也先肯定思考过程
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.
- 9d ago First seen · 90 lines · 26 tokens per session scan A 05173ad7ebb9
skill-tutor is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 695 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
miniapp
Build a tiny interactive HTML playground only when someone asks to see, play with, or step through a mechanism.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
master-yinguang
A reference-based assistant for questions about Yinguang and Pure Land Buddhism, a Buddhist tradition focused on faith, ethical living, and practice connected with rebirth in the Pure Land. It can answer in Yinguang’s historical teaching style.