by auto-use · Codex Skill · ★ 120
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🔒 Is Auto-Use safe to install? View the security audit →
# Auto Use 🤖 Computer Use Framework for macOS & Windows Let AI drive your computer — Autouse AI — Computer Use, now with both the macOS and Windows builds combined in a single repository. Control your entire OS with natural language. Browser automation, coding tasks, file management — all powered by vision-language models. Features • Architecture • GUI Engine • Example Tasks • Providers • Setup • Author ## ⭐ Star History ✨ Features GUI automation via hybrid Accessibility
| Stars | 120 |
| Forks | 13 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 66.8276214395415/100 |
| Last Updated | 2026-06-30 |
| Created | 2026-01-18 |
| Platforms | browser, claude-code, python |
| Est. Tokens | ~16k |
These tools work well together with Auto-Use for enhanced workflows:
Looking for a Auto-Use alternative? If you're comparing Auto-Use with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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Auto-Use is Auto-Use Computer Use — drives your OS, browser, scours the web, writes your code. One agent, end to end.. It is categorized as a Codex Skill with 120 GitHub stars.
Auto-Use is primarily written in Python. It covers topics such as agentic-ai, ai-agents, anthropic.
You can find installation instructions and usage details in the Auto-Use GitHub repository at github.com/auto-use/Auto-Use. The project has 120 stars and 13 forks, indicating an active community.
Auto-Use is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Auto-Use on Agent Skills Hub include os-ai-computer-use, open-cowork, clawdcursor. 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: