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 Syngnat · MCP Server · ★ 1.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GoNavi safe to install? View the security audit →
GoNavi Navigate every data source — native speed, agent-ready, zero Electron bloat. A high-performance cross-platform database client built with Wails (Go) + React. Desktop-first. MCP-ready. 30MB class binaries. <im
| Stars | 1,897 |
| Forks | 204 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 61.0701021312398/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-14 |
| Created | 2026-01-31 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~19k |
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GoNavi is High-performance multi-data-source database client — ~30MB, AI & MCP ready, zero Electron bloat. | 高性能多数据源数据库客户端：约 30MB，AI 与 MCP 就绪，告别 Electron 膨胀。. It is categorized as a MCP Server with 1.9k GitHub stars.
GoNavi is primarily written in TypeScript. It covers topics such as ai, cross-platform, database-client.
You can find installation instructions and usage details in the GoNavi GitHub repository at github.com/Syngnat/GoNavi. The project has 1.9k stars and 204 forks, indicating an active community.
GoNavi is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to GoNavi on Agent Skills Hub include tabularis, tabularis, full-stack-ai-agent-template. 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: