claude-computer-use-macos: MacOS Demo for Claude Computer Use

by PallavAg · Agent Tool · ★ 295

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

How to install claude-computer-use-macos

git clone https://github.com/PallavAg/claude-computer-use-macos.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 74/100

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.

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.

anthropicclaudecomputeruse

Quick Facts

Stars295
Forks58
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score74/100
Open Issues8
Last Updated2024-10-24
Created2024-10-23
Platformsclaude-code, python
Est. Tokens~1k

About claude-computer-use-macos

Claude Computer Use Demo for MacOS This repository contains a Python script that demonstrates Anthropic's Computer Use capabilities, modified to run on MacOS without requiring a Docker container. The script allows Claude 3.5 Sonnet to perform tasks on your Mac by simulating mouse and keyboard actions as well as running bash command. Forked from Anthropic's computer use demo - optimized for MacOS.

Compatible Skills

These tools work well together with claude-computer-use-macos for enhanced workflows:

  • TuriX-CUA — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (59%)
  • os-ai-computer-use — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (58%)
  • mcp-remote-macos-use — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (57%)
  • AnyTool — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)

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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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