codex-dream-skin — not yet audited

by xnydl · Codex Skill · ★ 54

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About codex-dream-skin

macOS & Windows Codex Desktop 梦幻换肤 Skill|由世事宜AI免费提供

codexcodex-desktopmacosopenaiskillssyaithemewindows

Quick Facts

Stars54
Forks2
LanguageJavaScript
CategoryCodex Skill
LicenseMIT
Quality Score57.1638194426598/100
Open Issues1
Last Updated2026-07-17
Created2026-07-15
Platformscodex, node
Est. Tokens~13k

codex-dream-skin alternative? Top 6 similar tools

Looking for a codex-dream-skin alternative? If you're comparing codex-dream-skin 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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  • codex-autoskin by Finderchangchang · ⭐ 118

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  • motive by geezerrrr · ⭐ 117

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  • alpha-insights by Ericyoung-183 · ⭐ 56

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  • open-responses-server by teabranch · ⭐ 184

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Frequently Asked Questions

What is codex-dream-skin?

codex-dream-skin is macOS & Windows Codex Desktop 梦幻换肤 Skill|由世事宜AI免费提供. It is categorized as a Codex Skill with 54 GitHub stars.

What programming language is codex-dream-skin written in?

codex-dream-skin is primarily written in JavaScript. It covers topics such as codex, codex-desktop, macos.

How do I install or use codex-dream-skin?

You can find installation instructions and usage details in the codex-dream-skin GitHub repository at github.com/xnydl/codex-dream-skin. The project has 54 stars and 2 forks, indicating an active community.

What license does codex-dream-skin use?

codex-dream-skin is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to codex-dream-skin?

The top alternatives to codex-dream-skin on Agent Skills Hub include codex-profiles, os-ai-computer-use, codex-autoskin. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools