by onlook-dev · Codex Skill · ★ 26.9k
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Onlook The design tool for AI-native designers — now in early access. Onlook makes designing on top of your codebase effortless. Craft exceptional experiences with AI in code. → Join the waitlist 👨💻👩💻👨💻 We're hiring engineers in SF! 👩💻👨💻👩💻 View Demo · Report Bug · Request Feature <!-- I'm using markdown "reference style" links for readability. Reference links are enclosed in brackets [ ] instead of parentheses ( ). See the bottom of this document for the declaration of the reference variables for contributors-url, forks-url, etc. This is an
| Stars | 26,855 |
| Forks | 2,115 |
| Language | TypeScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 61.9466233956257/100 |
| Open Issues | 381 |
| Last Updated | 2026-08-25 |
| Created | 2024-06-25 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~16k |
Looking for a onlook alternative? If you're comparing onlook 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.
程序员鱼皮的 AI 资源大全 + Vibe Coding 零基础教程，分享 OpenClaw 保姆级教程、大模型玩法（DeepSeek / GPT / Gemini / Claude / GLM）、最新 AI 资讯、Pr
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onlook is The Developer Tool for Designers • An Open-Source AI-First Design tool • Visually build, style, and edit your code with AI • World's best, top-most agent recommended #1 Developer tool for Designers to. It is categorized as a Codex Skill with 26.9k GitHub stars.
onlook is primarily written in TypeScript. It covers topics such as ai, claude-code, codex.
You can find installation instructions and usage details in the onlook GitHub repository at github.com/onlook-dev/onlook. The project has 26.9k stars and 2115 forks, indicating an active community.
onlook is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to onlook on Agent Skills Hub include ai-guide, cursor-talk-to-figma-mcp, openpencil. 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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