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 ofershap · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is real-browser-mcp safe to install? View the security audit →
real-browser-mcp The missing piece in AI coding: your agent can now see your REAL browser. <a href="https://github.com/ofershap/real-browser-mcp/actions/workflow
| Stars | 51 |
| Forks | 6 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.0686536471726/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-14 |
| Created | 2026-03-02 |
| Platforms | browser, claude-code, mcp, node |
| Est. Tokens | ~15k |
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real-browser-mcp is MCP server + Chrome extension that gives AI agents control of your real browser with existing sessions and logins. It is categorized as a MCP Server with 51 GitHub stars.
real-browser-mcp is primarily written in JavaScript. It covers topics such as ai, browser-automation, chrome-extension.
You can find installation instructions and usage details in the real-browser-mcp GitHub repository at github.com/ofershap/real-browser-mcp. The project has 51 stars and 6 forks, indicating an active community.
real-browser-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to real-browser-mcp on Agent Skills Hub include camofox-mcp, claude-historian-mcp, vscode-mcp. 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: