by iFurySt · MCP Server · ★ 2.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
| Stars | 2,459 |
| Forks | 229 |
| Language | Swift |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70/100 |
| Open Issues | 32 |
| Last Updated | 2026-10-17 |
| Created | 2026-04-17 |
| Platforms | claude-code, cli, codex, gemini, mcp |
| Est. Tokens | ~16k |
open-computer-use [!TIP] Interested in Browser Use? Check out open-browser-use. is an open-source service wrapped as . Any AI agent or MCP client can use it to run Computer Use on macOS, Linux, and Windows. This project was inspired by OpenAI's Codex Computer Use. It showed that non-intrusive CUA can be built on top of Accessibility, so I decided to build an open-source version.
These tools work well together with open-codex-computer-use for enhanced workflows:
Looking for a open-codex-computer-use alternative? If you're comparing open-codex-computer-use with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Agent Desktop gives any agent reliable computer use on the desktop. Built with Rust, it sees any app's real UI
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OpenGUI is an Android GUI agent framework for phone-use AI that can see, plan, and operate real mobile apps th
Full computer-use for AI agents. Self-learning workflows. Native macOS. No screenshots required.
Terminal session manager for AI coding agents. One TUI for Claude, Gemini, OpenCode, Codex, and more.
Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Wi
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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: