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 semantic-craft · Claude Skill · ★ 90
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is iOS-vibebuddy safe to install? View the security audit →
vibebuddy Leave your desk. Keep your agents moving. A native Mac, iPhone & Apple Watch companion for your AI workflow.Claude Code · Codex · Grok Build · Grok Bot · CursorFollow tasks, review supported requests, and keep account usage in view. Download for Mac · Get the iPhone app · Get started · 简体中文 Free & open source · No VibeBuddy account · No analytics · Bring your own AI key The actual Mac 1.3.19 interface in Demo mode. Tasks, projects and quota readings are sample data. A little less checking. A lot more doing. Start a refac
| Stars | 90 |
| Forks | 2 |
| Language | Swift |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 57.2779506116875/100 |
| Last Updated | 2026-09-22 |
| Created | 2026-06-04 |
| Platforms | claude-code, codex |
| Est. Tokens | ~18k |
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iOS-vibebuddy is Native Mac, iPhone & Apple Watch companion for Claude Code, Codex, Grok and Cursor. Task inbox, wrist notifications, agent handoffs, quota widgets, optional voice and summaries. Free and open source.. It is categorized as a Claude Skill with 90 GitHub stars.
iOS-vibebuddy is primarily written in Swift. It covers topics such as ai-agents, apple-watch, claude-code.
You can find installation instructions and usage details in the iOS-vibebuddy GitHub repository at github.com/semantic-craft/iOS-vibebuddy. The project has 90 stars and 2 forks, indicating an active community.
iOS-vibebuddy is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to iOS-vibebuddy on Agent Skills Hub include headroom, ContextKit, agf. 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: