Agentlas-OS: Desktop AI agent that hires a specialist for each task, works in your real apps for weeks, and takes orders

by agentlas-ai · Agent Tool · ★ 1.5k

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

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-orchestrationagentic-aiai-agentai-agentsai-assistantai-coworkerai-toolsautonomous-agentsclaudeclaude-code

Quick Facts

Stars1,465
Forks108
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score66/100
Open Issues1
Last Updated2026-11-07
Created2026-06-04
Platformsclaude-code, python
Est. Tokens~21k

About Agentlas-OS

Agentlas OS Agents are easy to create. Agentlas gives them an operating system. Run owned agents, borrow Hub specialists, control a real browser, preserve governed memory, and verify work on the LLMs you already use. Hephaestus is the open-source engine underneath.

Compatible Skills

These tools work well together with Agentlas-OS for enhanced workflows:

  • LiteMultiAgent — semantic(0.43)+complementary+same_lang+similar_pop+shared_platform (65%)
  • LiteWebAgent — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (63%)
  • amfs — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (60%)

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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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