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 naodeng · Agent Tool · ★ 223
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-qa-skills safe to install? View the security audit →
🇨🇳中文 | 🇬🇧English AI 测试辅助技能合集 这是一个按语言分区整理好的 AI 测试技能库，适合放到 Codex、Cursor、Claude Code、Kiro、OpenCode、Trae 等工具里直接使用。 在线访问地址：https://inaodeng.com/qaskills/ 你会在这里得到什么 中文与英文双版本技能 4 个工作流技能 25 个测试类型技能 可直接复制到常见 AI 工具中使用 核心目录 中文技能： 英文技能： 工作流技能： 测试类型技能： 5 分钟开始 克隆项目 复制一个技能到你的 AI 工具 在 AI 工具里调用 推荐技能入口（中文） 工作流技能 测试类型技能 skills/zh/tes
| Stars | 223 |
| Forks | 31 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 63.6948881485811/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-22 |
| Created | 2026-02-04 |
| Platforms | python |
| Est. Tokens | ~14k |
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awesome-qa-skills is Awesome QA Skills is an open-source AI-native Quality Engineering skill system that helps engineering teams reason about, design, execute, evaluate, and continuously improve software quality across th. It is categorized as a Agent Tool with 223 GitHub stars.
awesome-qa-skills is primarily written in Python. It covers topics such as agent-skills, ai-qa, ai-skills.
You can find installation instructions and usage details in the awesome-qa-skills GitHub repository at github.com/naodeng/awesome-qa-skills. The project has 223 stars and 31 forks, indicating an active community.
The top alternatives to awesome-qa-skills on Agent Skills Hub include awesome-agent-skills, claudepro-directory, awesome-claude-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: