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 TaiT-tt · Codex Skill · ★ 285
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tait-crt-interface-skill safe to install? View the security audit →
TaiT CRT Interface Skill TaiT-CRT-Interface-Skill是一个Codex的图片生成skill，将用户上传的人像、摄影或文字描述等主题设计成一张带有早期CRT计算机界面质感的复古插画。 TaiT-CRT-Interface-Skill is a Codex image generation skill that transforms user-uploaded portraits, photographs, or text descriptions into a retro illustration with the feel of an early CRT computer interface. 视觉特征 一个占据画面主要面积的像素风主体，作为无边框系统壁纸 早期 Macintosh、Minitel 与 8-bit 计算机界面语言 3–6 个大小、比例和位置各异的悬浮视窗 1–3 个用于展示五官、饰品或局部特征的元素提取视窗 主体、文字、图标和窗口共用同一个方形像素网格 棋盘格光学灰度、硬边锯齿、扫描线、辉光、噪点和信号干扰 四周固定的 CRT 球面桶形畸变 多套预设色卡，也可以根据上传图片自动创作 2–5 色 生成示例 / Examples 参考图 tait-crt-interface生成 参考图 tait-crt-interface生成 <img src="生成示例/极客01a.png" a
| Stars | 285 |
| Forks | 13 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 58.5774572073483/100 |
| Last Updated | 2026-08-11 |
| Created | 2026-08-02 |
| Platforms | codex, python |
| Est. Tokens | ~14k |
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tait-crt-interface-skill is TaiT-CRT-Interface-Skill is a Codex image generation skill that transforms user-uploaded portraits, photographs, or text descriptions into a retro illustration with the feel of an early CRT computer i. It is categorized as a Codex Skill with 285 GitHub stars.
tait-crt-interface-skill is primarily written in Python.
You can find installation instructions and usage details in the tait-crt-interface-skill GitHub repository at github.com/TaiT-tt/tait-crt-interface-skill. The project has 285 stars and 13 forks, indicating an active community.
The top alternatives to tait-crt-interface-skill on Agent Skills Hub include Skills-Manager, ai-maestro, upskill. 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.
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