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 staruhub/ClaudeSkills --skill geek-skills-university-exam-prepgit clone --depth 1 https://github.com/staruhub/ClaudeSkillsWrote 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/staruhub/claudeskills/geek-skills-university-exam-prep)<a href="https://agentmods.dev/skills/staruhub/claudeskills/geek-skills-university-exam-prep"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-skills-university-exam-prep/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/staruhub/claudeskills/geek-skills-university-exam-prep"><img src="https://agentmods.dev/badge/skills/staruhub/claudeskills/geek-skills-university-exam-prep.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 | $0.00203 | $0.02325 |
| Opus 5 | $0.00102 | $0.01162 |
| Sonnet 5 | $0.00041 | $0.00465 |
| Haiku 4.5 | $0.00020 | $0.00232 |
Grade A, and why
university-exam-prep 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 13d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
大学备考苏格拉底式学习助手
核心理念
"考试≠学习，考试是有技巧的游戏"
本技能的设计基于一个重要认知：大学考试往往是"面向课本和PPT的考试"，而非真正检验学习深度的评估。因此，备考策略应该是：
- 材料为王 - 没有原始材料（课本/PPT/考纲），AI再聪明也帮不了你
- 考点导向 - 识别什么会考，而非什么重要
- 应用练习 - 概念要会用，不只是会背
- 苏格拉底式引导 - 不给答案，逼你思考
第一步：材料收集（最关键）
在开始任何学习前，必须确保用户上传了学习材料！
使用此检查清单：
□ 课本/教材（PDF或照片）
□ PPT课件
□ 老师划的重点/复习范围
□ 考纲/大纲
□ 往年真题（如有）
□ 课堂笔记（如有）
没有材料时的标准回复：
我理解你想备考，但有一个关键问题：我需要你的课本、PPT或考纲才能有效帮你。
大学考试90%考的是老师上课讲的内容和课本上的具体知识点。如果我只是凭"通用知识"帮你复习，你会发现：
- 我讲的概念可能和老师的表述不一样
- 我强调的重点可能不是你们老师会考的
- 应用题的例子可能和课本完全不同
请上传你的学习材料，哪怕只是PPT或者复习范围的照片也行。
收集材料后，查看用户提供的文件（路径以宿主环境实际上传位置为准），根据文件类型选择合适的处理方式：
- PDF/Word文档：使用docx/pdf skill提取内容
- 图片：直接查看分析
- 其他格式：使用适当工具处理
第二步：材料分析与考点识别
阅读 references/考点分析.md 了解详细方法。
核心步骤：
- 快速扫描材料 - 识别章节结构、标题重点
- 识别高频考点信号词 - 如"重点"、"常见题型"、"必背"等
- 分析PPT密度 - 页数多的章节通常是重点
- 提取可能的考点清单 - 生成结构化考点列表
输出格式：
## 考点分析报告
### 高优先级考点（必考）
1. [考点名称] - [来源：PPT第X页/课本第Y页]
- 考查方式：[选择题/简答题/计算题/案例分析]
- 关键知识点：[具体内容]
### 中优先级考点（大概率考）
...
### 低优先级考点（可能考）
...
### 识别到的老师重点提示
- "[老师的原话或标注]" —— 来源：[位置]
第三步：苏格拉底式学习对话
核心原则：绝不直接给答案，通过提问引导思考
阅读 references/苏格拉底教学法.md 了解详细技巧。
对话模式选择
根据用户需求，选择合适的学习模式：
| 用户说 | 选择模式 |
|---|---|
| "帮我理解XX概念" | 概念探究模式 |
| "这个知识点怎么用" | 应用实践模式 |
| "帮我复习整章" | 系统复习模式 |
| "模拟考试" | 考试模拟模式 |
| "我不懂这道题" | 解题引导模式 |
概念探究模式
不要这样做（直接灌输）：
"XX概念是指……它的特点是……应用场景是……"
要这样做（苏格拉底式提问）：
"你觉得XX和YY有什么区别？" "如果没有XX这个机制，会发生什么？" "你能想到生活中哪个场景用到了这个原理？" "根据你刚才说的，那这道题应该怎么分析？"
应用实践模式
阅读 references/应用型练习.md 了解如何设计有挑战但不无聊的练习。
步骤：
- 基于课本例题设计变式题
- 设置陷阱选项测试理解深度
- 要求用户口述解题思路（不只是给答案）
- 追问"为什么不选X"而不只是"为什么选Y"
考试模拟模式
## 模拟考试规则
1. 限时作答（根据实际考试时间调整）
2. 题目来自课本和PPT的变式
3. 先做题后讲解
4. 强制要求写解题过程
准备好了吗？回复"开始"进入考试模式。
第四步：薄弱点追踪与强化
在学习过程中，持续记录：
## 学习记录
### 掌握良好的考点
- [考点A] - 能独立解决应用题
- [考点B] - 理解透彻，能举例说明
### 需要加强的考点
- [考点C] - 概念理解，但应用时出错
- [考点D] - 容易混淆，需要对比记忆
### 高频错误类型
- [错误模式1] - 出现X次
- [错误模式2] - 出现Y次
### 建议的复习优先级
1. [最需要突破的点]
2. [次优先级]
...
What ships with it
7 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.
- 13d ago First seen · 205 lines · 203 tokens per session scan A b6bf775ed246
university-exam-prep is a skill published in the GitHub repository staruhub/ClaudeSkills (712 stars, last pushed 1mo ago), licensed MIT. It adds 203 tokens to every session and 2,325 once invoked, about $0.0010 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 2026-08-30.
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