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 thebanri · MCP Server · ★ 151
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is limoni safe to install? View the security audit →
🍋 Limoni A terminal UI engine for Go that tests can click, AI agents can drive,and the garbage collector never sees. English • Türkçe ▶ Try it in your browser — the same engine compiled to WebAssembly, no install. <
| Stars | 151 |
| Forks | 11 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 64.8642164679472/100 |
| Open Issues | 6 |
| Last Updated | 2026-10-04 |
| Created | 2026-08-06 |
| Platforms | browser, cli, go, mcp |
| Est. Tokens | ~19k |
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limoni is Terminal UI engine for Go that tests can click and AI agents can drive (MCP). Zero-allocation rendering, immediate mode + Elm architecture, 3D, images, charts, accessibility, WebAssembly.. It is categorized as a MCP Server with 151 GitHub stars.
limoni is primarily written in Go. It covers topics such as accessibility, ai-agents, ansi.
You can find installation instructions and usage details in the limoni GitHub repository at github.com/thebanri/limoni. The project has 151 stars and 11 forks, indicating an active community.
limoni is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to limoni on Agent Skills Hub include showagent, purple, mcpsnoop. 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: