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 AiTerminalFoundation · Agent Tool · ★ 149
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-terminal safe to install? View the security audit →
AI Terminal A Tauri + Angular terminal application with integrated AI capabilities. Features Natural language command interpretation Integrated AI assistant Command history and auto-completion Cross-platform support (macOS, Windows, Linux) Modern UI built with Tauri and Angular Requirements Node.js 18+ Rust and Cargo For AI features: Ollama (can be installed with ) Development Setup Clone the repository: Install dependencies and run the project: Installation macOS (Homebrew) You can install AI Terminal using Homebrew: After installation, you can launch the application from Spotlight or run it from the terminal: Quick Guide to Using Ollama to Download Model Linux Install Ollama Open your terminal and run: Download the Model Run the following command: macOS Download Ollama Visit Ollama download page. Click Download for macOS. Install Ollama Open the downloaded
| Stars | 149 |
| Forks | 14 |
| Language | Rust |
| Category | Agent Tool |
| Quality Score | 63.9018812591971/100 |
| Open Issues | 63 |
| Last Updated | 2026-05-09 |
| Created | 2025-03-15 |
| Platforms | cli, rust |
| Est. Tokens | ~4296k |
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ai-terminal is Your AI mate into your favourite terminal. It is categorized as a Agent Tool with 149 GitHub stars.
ai-terminal is primarily written in Rust. It covers topics such as agent-cli, ai-agents, ai-chatbot.
You can find installation instructions and usage details in the ai-terminal GitHub repository at github.com/AiTerminalFoundation/ai-terminal. The project has 149 stars and 14 forks, indicating an active community.
The top alternatives to ai-terminal on Agent Skills Hub include best-of-ai, aibooks, 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: