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 HKUSTDial · AI Tool · ★ 6.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Supervisor-Skills safe to install? View the security audit →
Supervisor-Skills: 将博导十年科研经验，炼化为你的 AI 副导师。 English · 中文 📰 News - [2026-07-15] 🤝 团队与豆包达成合作，Supervisor-Skills 正式作为豆包·办公模式的核心科研技能完成部署，驱动「科研评估」与「学术创作」两大官方能力，将本项目沉淀的科研方法论带给亿级用户。 - [2026-07-10] 🚀 v2.1：新增 （证据门控的论文正文写作）、（忠于原意的语言润色）、（综述级文献调研）三个技能； 升级为正文输出；评估与评审技能引入学科范式路由。详见 CHANGELOG。 - [2026-07-03] 🎤 受邀在 2026 中国智能体大会 Skill 会场进行分享。 - [2026-07-03] 🧩 新增 技能：参考图一键重建为可编辑 Draw.io。 - [2026-05-21] ⭐ GitHub Star 突破 1,000（发布第 32 天）。 - [2026-04-19] 🎉 项目发布：七个锚点技能覆盖论文全生命周期，配套 handbook 系统课程。 为什么做这个项目？ 大家好，我是骆昱宇，香港科技大学（广州）的助理教授。从自己读博到指导学生，我反复看到一个场景：许多才华横溢的研究生，在踏入科研大门时，常常被相似的困境所困扰： 理论与实践脱节：读了再多"科研指南"，面对自己的课题时，依旧不知如何下笔。
| Stars | 6,852 |
| Forks | 435 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 57.6930580502307/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-05 |
| Created | 2026-04-19 |
| Platforms | python |
| Est. Tokens | ~15k |
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Supervisor-Skills is 将博导十年科研经验炼化为可直接调用的 AI 技能。从 Idea 构思到论文投稿，你的 AI 科研副导师。. It is categorized as a AI Tool with 6.9k GitHub stars.
Supervisor-Skills is primarily written in Python. It covers topics such as ai-research, ai-researcher, awesome.
You can find installation instructions and usage details in the Supervisor-Skills GitHub repository at github.com/HKUSTDial/Supervisor-Skills. The project has 6.9k stars and 435 forks, indicating an active community.
The top alternatives to Supervisor-Skills on Agent Skills Hub include awesome-agent-skills, awesome-claude-skills, AI-Research-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: