by LvcidPsyche · MCP Server · ★ 922
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Featured in: browser-use, Playwright MCP & AI Browser Agents
git clone https://github.com/LvcidPsyche/auto-browser.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 | 922 |
| Forks | 154 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66/100 |
| Last Updated | 2026-11-07 |
| Created | 2026-03-09 |
| Platforms | browser, claude-code, docker, mcp, python |
| Est. Tokens | ~15k |
Auto Browser Give your AI agent a real browser, with a human in the loop. Auto Browser is an MCP-native browser control plane for authorized workflows. It gives MCP clients, LLM agents, and operators a shared Playwright browser with human takeover, reusable auth profiles, approvals, audit trails, and local-first deployment.
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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: