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 oil-oil · Agent Tool · ★ 171
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is vibe-hub-skill safe to install? View the security audit →
在编程交流中识别专业术语，提供通俗解释和可追溯的术语链接，帮助准确表达需求。 它不提供学习路线、课程计划或本地练习，也不会把一句简单需求扩展成教程。 一分钟安装 把下面这段话完整发给你的 Agent： 两个核心功能 把模糊描述改成准确需求 你可以说： Skill 会保留原意，并把需求改成类似这样： 找到明确术语时，第一次出现会直接链接到 VibeHub。Skill 不会擅自增加框架、组件库、参数或实现方案。 Skill 不会把整句需求直接丢给搜索接口。它会先结合当前语境推断候选术语，再用一次批量查询验证名称和链接；高置信度时只查一个，存在歧义时最多比较三个。 主动提示相关术语 你不需要先问“这个叫什么”。即使 Agent 已经完成了一部分代码，你又追加了一句口语化需求，Skill 也会在继续开发的同时指出最相关的术语： Skill 会提示： 术语的作用是让沟通更准确，不是展示词汇量。没有真正有帮助的术语时，Skill 不会硬塞链接。 看不懂 Agent 回复时 把 Agent 的原话贴出来即可： VibeHub 会选出最影响下一步判断的概念，解释它在当前任务中指什么、负责什么，以及一个容易混淆的边界。它不会逐词翻译整段回复，也不会自动开始课程。 可以这样问 工作原则 先给可以直接使用的结果，再解释术语 保留用户原意，不擅自增加实现细节 每次最多提示 1–3 个真正相关的术语 只使用 VibeHub 返回的页面链接，不伪造地址 用户表达可能模糊时，主动指出准确术语，但不打断当前任务 用户表达已经准确且没有可补充的术语时，不硬加术语教学 数据与
| Stars | 171 |
| Forks | 12 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.7673445174282/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-10 |
| Created | 2026-07-23 |
| Platforms | node |
| Est. Tokens | ~9k |
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vibe-hub-skill is 在编程交流中识别专业术语，提供通俗解释和可追溯的术语链接，帮助准确表达需求。. It is categorized as a Agent Tool with 171 GitHub stars.
vibe-hub-skill is primarily written in JavaScript. It covers topics such as agent-skill, education, vibe-coding.
You can find installation instructions and usage details in the vibe-hub-skill GitHub repository at github.com/oil-oil/vibe-hub-skill. The project has 171 stars and 12 forks, indicating an active community.
vibe-hub-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to vibe-hub-skill on Agent Skills Hub include octocode-mcp, vibe, human-skill-tree. 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: