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 modelscope · Agent Tool · ★ 438
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-Vibe-Research safe to install? View the security audit →
🔬 Awesome Vibe Research 面向 AI 辅助科研的开放共建仓库 收集和沉淀科研全流程中的 agents、skills、workflows、tools 与最佳实践 🇨🇳 中文版 | 🇬🇧 English 🧪 全流程 • 🔭 方向扫描 • 📚 文献研究 • 🧩 方法设计 • ⚗️ 实验执行 • 📊 可视化 • ✍️ 写作 • 📦 复现发布 • 📡 传播 • 🤝 贡献 我们关注能在科研工作中反复复用、能被同行验证、能逐步演化、表现最优的 AI 辅助能力。 本仓库从「与 Agent 共事做研究」系列沙龙出发，希望把 speaker 的实践经验、观众的使用反馈、开源项目与可复用流程沉淀成一个社区可维护的知识库。 🗺️ 科研流程地图 下表是我们对科研生命周期的 9 阶段拆分。每个阶段列出了"典型问题"和"可沉淀的 AI 辅助组件类型"。正文按阶段展开条目表，收录已知最好的项目、skill、workflow。 💡 如果你觉得某个阶段的条目缺失或可以补充——直接在对应表格中添加一行。 如
| Stars | 438 |
| Forks | 51 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 53.2158487792423/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-22 |
| Created | 2026-06-12 |
| Platforms | python |
| Est. Tokens | ~16k |
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Awesome-Vibe-Research is An open, collaboratively-built repository for AI-assisted scientific research — collecting and curating agents, skills, workflows, tools, and best practices across the full research lifecycle. 面向 AI 辅. It is categorized as a Agent Tool with 438 GitHub stars.
Awesome-Vibe-Research is primarily written in Python.
You can find installation instructions and usage details in the Awesome-Vibe-Research GitHub repository at github.com/modelscope/Awesome-Vibe-Research. The project has 438 stars and 51 forks, indicating an active community.
Awesome-Vibe-Research is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-Vibe-Research on Agent Skills Hub include skills, awesome-android-agent-skills, claude-code-skill-factory. 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: