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 Wholiver · Codex Skill · ★ 208
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is swiftui-design-skill safe to install? View the security audit →
SwiftUI Design Skill "One prompt. A shippable SwiftUI interface." Type one line in your agent. Get back a SwiftUI interface that doesn't look AI-generated. In 3 to 30 minutes, you can design a signature iOS interface — not "AI-made-it-looks-okay" quality, but something that looks like it was crafted by a designer with taste. Anti-AI Slop 6 Iron Rules, Design Direction Advisor, Brand Asset Protocol, 5-Dimension Review — all built in. Give the skill your brand palette and it will read your vibe; give nothing and the 5 built-in design languages will still hold their ground. Works across agents — Claude Code, Cursor, Codex, OpenCode, Hermes, all supported. Install & Go What Can It Do 10–15 m
| Stars | 208 |
| Forks | 9 |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 66.7290615594978/100 |
| Last Updated | 2026-05-01 |
| Created | 2026-04-30 |
| Platforms | claude-code, codex |
| Est. Tokens | ~5k |
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swiftui-design-skill is SwiftUI Front-End Design Skills — Six Ironclad Rules Against AI Sloppiness, Design Direction Consulting, Brand Asset Guidelines, and Five-Dimensional Review. Supports all AI agent platforms, including. It is categorized as a Codex Skill with 208 GitHub stars.
You can find installation instructions and usage details in the swiftui-design-skill GitHub repository at github.com/Wholiver/swiftui-design-skill. The project has 208 stars and 9 forks, indicating an active community.
swiftui-design-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to swiftui-design-skill on Agent Skills Hub include swift-ios-skills, avoid-ai-design, TokenClock. 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: