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 chrichuang218 · Codex Skill · ★ 213
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-learning-coach safe to install? View the security audit →
AI Learning Coach Turn any real project into a personalized learning journey. 你只需要坐下来并说“开始学习”，教练负责理解你的背景、选择真实项目、找到合适入口，一次一步带你真正学会。 实时对话 + 真实源码：教练选择当前入口，用户围绕代码连续追问并建立理解。 长期学习记忆：用 Markdown 记录掌握证据、项目进度、贡献日历和剩余时间。 AI Learning Coach 是一个中文优先的 Codex 私人学习教练插件。它不把课程设计责任推给用户，也不默认生成教程、HTML 课程或完整任务单，而是通过真实项目、持续对话、Run、Debug 和掌握证据动态推进学习，并用纯 Markdown 显示目标进度、项目进度、学习节奏和剩余时间。 首要服务对象是希望通过真实代码学习 TypeScript、AI Agent、源码阅读和新技术栈的开发者。相同方法也适用于写作、语言、考试、研究和职业技能。 快速开始 以下命令已在 Codex CLI 验证： bash codex plugin marketplace add chrichuang218/ai-learning-coach codex plugin add ai-le
| Stars | 213 |
| Forks | 9 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.8658576938996/100 |
| Last Updated | 2026-09-06 |
| Created | 2026-06-28 |
| Platforms | codex, python |
| Est. Tokens | ~15k |
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ai-learning-coach is AI private learning coach for Codex: learn through real projects, adaptive dialogue, debugging, evidence-based mastery, and visible progress.. It is categorized as a Codex Skill with 213 GitHub stars.
ai-learning-coach is primarily written in Python. It covers topics such as active-learning, agent-skills, ai-learning.
You can find installation instructions and usage details in the ai-learning-coach GitHub repository at github.com/chrichuang218/ai-learning-coach. The project has 213 stars and 9 forks, indicating an active community.
ai-learning-coach is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-learning-coach on Agent Skills Hub include learn-anything-with-AI, Skills-Manager, ok-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: