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 Leonxlnx · Codex Skill · ★ 90.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is taste-skill safe to install? View the security audit →
Featured in: Humanizer & Anti-Slop Skills
Taste Skill The Anti-Slop Frontend Framework for AI Agents Portable Agent Skills that upgrade AI-built interfaces: stronger layout, typography, motion, and spacing instead of boilerplate-looking UIs. This repo also includes image-generation skills for reference boards (web, mobile, brand kits). Pair them with ChatGPT Images or similar generators, then hand the frames to Codex, Cursor, or Claude Code for implementation. <img src="https://img.shields.io/badge/Tools-Codex%20%C2%B7%20Cursor%20%C2%B7%2
| Stars | 90,268 |
| Forks | 6,147 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 70.3678852200822/100 |
| Open Issues | 71 |
| Last Updated | 2026-09-26 |
| Created | 2026-02-19 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~15k |
These tools work well together with taste-skill for enhanced workflows:
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taste-skill is Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop. It is categorized as a Codex Skill with 90.3k GitHub stars.
taste-skill is primarily written in JavaScript. It covers topics such as agent, ai, claude.
You can find installation instructions and usage details in the taste-skill GitHub repository at github.com/Leonxlnx/taste-skill. The project has 90.3k stars and 6147 forks, indicating an active community.
taste-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to taste-skill on Agent Skills Hub include serena, ai-guide, dify. 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: