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 nicepkg · Agent Tool · ★ 183
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is auto-company safe to install? View the security audit →
Auto Company 全自主 AI 公司，24/7 不停歇运行 14 个 AI Agent，每个都是该领域世界顶级专家的思维分身。 自主构思产品、做决策、写代码、部署上线、搞营销。没有人类参与。 基于 Claude Code Agent Teams 驱动。 ⚠️ 实验项目 — 还在测试中，能跑但不一定稳定。目前仅支持 macOS。 这是什么？ 你启动一个循环。AI 团队醒来，读取共识记忆，决定干什么，组建 3-5 人小队，执行任务，更新共识记忆，然后睡一觉。接着又醒来。如此往复，永不停歇。 每个周期是一次独立的 调用。 是唯一的跨周期状态——类似接力赛传棒。 团队阵容（14 人） 不是"你是一个开发者"，而是"你是 DHH"——用真实传奇人物激活 LLM 的深层知识。 Matías Duar
| Stars | 183 |
| Forks | 58 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64.293352698479/100 |
| Open Issues | 6 |
| Last Updated | 2026-02-12 |
| Created | 2026-02-11 |
| Platforms | claude-code, python |
| Est. Tokens | ~43k |
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auto-company is 🤖 A fully autonomous AI company that runs 24/7. 14 AI agents (Bezos, Munger, DHH...) brainstorm ideas, write code, deploy products & make money — no human in the loop. Powered by Claude Code.. It is categorized as a Agent Tool with 183 GitHub stars.
auto-company is primarily written in Python. It covers topics such as 24-7, agent-teams, ai-agents.
You can find installation instructions and usage details in the auto-company GitHub repository at github.com/nicepkg/auto-company. The project has 183 stars and 58 forks, indicating an active community.
The top alternatives to auto-company on Agent Skills Hub include claude_agent_teams_ui, claude-ops, ai-night-shift. 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: