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 myshell-ai · Agent Tool · ★ 1.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AIlice safe to install? View the security audit →
Ailice Quick Start • Demo • Development • Twitter • Reddit ATTENTION! We currently has no plans to create any related crypto tokens. Please be cautious and recognize scams to avoid being deceived. (Updated on January 6, 2025) :fire: Mar 22, 2025: Ailice can now use MCP tools! Click here. :fire: Jan 23, 2025: Updated the voice dialogue feature. Thanks to ChatTTS's excellent implementation, voice dialogue has finally moved beyond its
| Stars | 1,414 |
| Forks | 205 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.3433811220963/100 |
| Open Issues | 6 |
| Last Updated | 2025-08-18 |
| Created | 2023-10-16 |
| Platforms | python |
| Est. Tokens | ~253k |
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AIlice is AIlice is a fully autonomous, general-purpose AI agent.. It is categorized as a Agent Tool with 1.4k GitHub stars.
AIlice is primarily written in Python. It covers topics such as agent, ai, llm.
You can find installation instructions and usage details in the AIlice GitHub repository at github.com/myshell-ai/AIlice. The project has 1.4k stars and 205 forks, indicating an active community.
AIlice is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AIlice on Agent Skills Hub include cursor-talk-to-figma-mcp, Viper, AReaL. 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: