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 chyinan · MCP Server · ★ 111
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Kokoro-Engine safe to install? View the security audit →
简体中文 Русский Kokoro Engine Open-source immersive character engine for desktop AI companions. 为每一位想拥有专属AI聊天伴侣的用户打造的跨平台虚拟角色交互引擎。 快速开始 · 下载安装 · 架构 · 贡献 Kokoro Engine 的独到之处 Kokoro Engine 不是“聊天壳子 + 桌宠皮肤”。它是一个完整的桌面角色运行时： All-in-one：Live2D、LLM、TTS、STT等技术集成在一个运行时闭环。 Built for extensibility：高自由度 MOD 系统 + MCP 协议，天然面
| Stars | 111 |
| Forks | 8 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 59.9558362369587/100 |
| Last Updated | 2026-07-12 |
| Created | 2026-02-16 |
| Platforms | mcp, node |
| Est. Tokens | ~6385k |
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Kokoro-Engine is Cross-platform virtual character immersive interaction engine. It is categorized as a MCP Server with 111 GitHub stars.
Kokoro-Engine is primarily written in TypeScript. It covers topics such as ai, custom, engine.
You can find installation instructions and usage details in the Kokoro-Engine GitHub repository at github.com/chyinan/Kokoro-Engine. The project has 111 stars and 8 forks, indicating an active community.
Kokoro-Engine is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Kokoro-Engine on Agent Skills Hub include neurolink, cadis, agnix. 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: