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 feder-cr · MCP Server · ★ 31.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is invisible_playwright_mcp safe to install? View the security audit →
AIHawk is an anti detect browser and web browsing agent, open source, with an MCP server for coding agents: undetected, no captchas, no blocks. Tell it what you want in plain language. FEATURED IN Business Insider · TechCrunch · Semafor · Wired · The Verge · Vanity Fair · 404 Media Two ways to use this browser agent From your assistant, over MCP Windows, in PowerShell: powershell powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" $env:Pa
| Stars | 31,765 |
| Forks | 4,687 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.9525281672984/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-03 |
| Created | 2024-08-04 |
| Platforms | browser, claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~15k |
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invisible_playwright_mcp is Playwright MCP server undetected by anti-bots and captchas: AI agent browses the web on anti-detect stealth Firefox, Python, undetected browser automation, scraping, computer use.. It is categorized as a MCP Server with 31.8k GitHub stars.
invisible_playwright_mcp is primarily written in Python. It covers topics such as agentic-ai, ai-agent, ai-automation.
You can find installation instructions and usage details in the invisible_playwright_mcp GitHub repository at github.com/feder-cr/invisible_playwright_mcp. The project has 31.8k stars and 4687 forks, indicating an active community.
invisible_playwright_mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to invisible_playwright_mcp on Agent Skills Hub include Scrapling, ruflo, career-ops. 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: