liliumai: Lilium AI: The ultimate personal AI agent framework for autonomous computer control

by beidald · Agent Tool · ★ 93

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install liliumai

git clone https://github.com/beidald/liliumai.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-frameworkai-agentai-agentsautomationautonomous-agentsbrowser-automationchatbotllmllm-productivitylocal-ai

Quick Facts

Stars93
Forks12
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score66/100
Last Updated2026-02-25
Created2026-02-16
Platformsbrowser, node
Est. Tokens~16k

About liliumai

Lilium AI English | 简体中文 Not just a chatbot, but your AI Co-pilot for total computer automation. liliumai.cn Lilium AI is a powerful personal AI agent framework designed for autonomous execution. It connects Large Language Models (LLMs) like OpenAI and Ollama to browser automation, system-level shell control, deep file integration, and omni-channel communication, enabling AI to perform complex...

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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