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 rayl15 · Codex Skill · ★ 116
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenVision safe to install? View the security audit →
OpenVision The open-source iOS app connecting Meta Ray-Ban smart glasses to AI assistants — cloud or fully on-device. Your glasses. Your AI. Your rules. Demo What Can You Do? "Ok Vision, what am I looking at?" "Is my form correct?" - real-time
| Stars | 116 |
| Forks | 15 |
| Language | Swift |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.5827798373848/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-17 |
| Created | 2026-02-09 |
| Platforms | browser, gemini |
| Est. Tokens | ~18k |
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OpenVision is Open-source iOS app connecting Meta Ray-Ban smart glasses to AI — 5 backends (on-device MLX models, Apple Intelligence, OpenAI, Gemini Live, OpenClaw), on-device neural voice, face recognition & live . It is categorized as a Codex Skill with 116 GitHub stars.
OpenVision is primarily written in Swift. It covers topics such as ai-assistant, apple-intelligence, face-recognition.
You can find installation instructions and usage details in the OpenVision GitHub repository at github.com/rayl15/OpenVision. The project has 116 stars and 15 forks, indicating an active community.
OpenVision is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenVision on Agent Skills Hub include ovo-local-llm, Artemis, SAM. 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: