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 xichan96 · Codex Skill · ★ 714
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dinotty safe to install? View the security audit →
Dinotty English | 中文 为 Coding Agent 打造的多端同步终端服务器。在任意设备上运行 Claude Code、opencode、Codex 或 OpenClaw，桌面端专业高效，移动端随时掌控——无缝切换，会话永不丢失。 截图 为什么选择 Dinotty？ 终端 Coding Agent（Claude Code、
| Stars | 714 |
| Forks | 74 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.4795522551245/100 |
| Open Issues | 20 |
| Last Updated | 2026-10-03 |
| Created | 2026-05-12 |
| Platforms | browser, claude-code, codex, node |
| Est. Tokens | ~15k |
These tools work well together with dinotty for enhanced workflows:
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dinotty is Multi-device terminal server for AI coding agents. Server-side VTE, session persistence, file browser, web preview, plugin system. Self-hosted.. It is categorized as a Codex Skill with 714 GitHub stars.
dinotty is primarily written in TypeScript. It covers topics such as ai-coding, axum, claude-code.
You can find installation instructions and usage details in the dinotty GitHub repository at github.com/xichan96/dinotty. The project has 714 stars and 74 forks, indicating an active community.
dinotty is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dinotty on Agent Skills Hub include pebrel, codegraph-rust, nebula. 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: