No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by IssacW228 · Agent Tool · ★ 196
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is student-llm-wiki safe to install? View the security audit →
📚 Student LLM Wiki 用 AI 把课件变成你自己的知识库 Turn your course slides into a personal wiki with AI 中文 · English 中文 这是什么？ 你把课件 PDF 丢进 文件夹，AI 自动帮你整理成结构化的 wiki 笔记，存在 里。你用 Obsidian 浏览这些笔记，用 AI 工具来复习、出题、检查薄弱环节。你不需要手写任何笔记。 基于 Andrej Karpathy 的 LLM Wiki 模式。 你需要准备的东西 1. Obsidian（用来浏览笔记） 下载地址：https://obsidian.md/download 免费，支持 Windows / macOS / Linux / iOS / Android 安装后还需要在社区插件里安装 Dataview（用于首页动态面板） 2. 选一个 AI 工具（任选其一即可） 3. 你的课件（PDF 或其他文本格式） 第一步：下载项目，用 Obsidian 打开 然后打开 Obsidian → 「打开本地仓库」→ 选择 文件夹。 必须安装 Dataview 插件：Obsidian 设置 → 社区插件 → 浏览 → 搜索 → 安装并启用。 第二步：选择你的 AI 工具并打开项目 Claude Code CLI（命令行） 确保已安装 Claude Code CLI 并登录 在项目目录运行： 配置文件（）会自动加载，
| Stars | 196 |
| Forks | 4 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.2107353004211/100 |
| Last Updated | 2026-06-16 |
| Created | 2026-06-01 |
| Platforms | claude-code |
| Est. Tokens | ~11k |
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student-llm-wiki is 📚 Student LLM Wiki — AI-compiled knowledge base for university students. Drop course slides, get a persistent interlinked wiki. Feynman review, exam prep, confidence decay, cross-course connections. . It is categorized as a Agent Tool with 196 GitHub stars.
You can find installation instructions and usage details in the student-llm-wiki GitHub repository at github.com/IssacW228/student-llm-wiki. The project has 196 stars and 4 forks, indicating an active community.
student-llm-wiki is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to student-llm-wiki on Agent Skills Hub include swarmvault, llm-wiki-plugin, llm-wiki-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: