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 devswha · Codex Skill · ★ 358
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is patina safe to install? View the security audit →
patina Strip the AI packaging. Keep the meaning. 한국어 · 中文 · 日本語 · English Try the browser audit — no install patina is a deterministic, pattern-based humanizer for Korean, English, Chinese, and Japanese. It finds AI-sounding phrasing and rewrites it without changing the claim, numbers, polarity
| Stars | 358 |
| Forks | 36 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 68.707943727384/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-02-24 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~15k |
These tools work well together with patina for enhanced workflows:
Looking for a patina alternative? If you're comparing patina with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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patina is AI-writing humanizer for KO/EN/ZH/JA. It is categorized as a Codex Skill with 358 GitHub stars.
patina is primarily written in JavaScript. It covers topics such as ai-detection, ai-humanizer, ai-writing.
You can find installation instructions and usage details in the patina GitHub repository at github.com/devswha/patina. The project has 358 stars and 36 forks, indicating an active community.
patina is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to patina on Agent Skills Hub include humanizer-ru, humanizer-skill, humanizer-de. 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: