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 keon · Agent Tool · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is browser-control safe to install? View the security audit →
browser-control A tiny, fast Rust CLI that drives a real browser over the Chrome DevTools Protocol — built for coding agents. No LLM. No MCP requirement. No framework. Just compact, composable browser commands you can pipe from a shell. exposes a browser as a small, debuggable pipe: page snapshots, stable element refs, raw CDP, a background event watcher, and shell-runnable scripts. The coding agent stays the agent — just gives it hands and eyes. bash browser-control launch https://example.com browser-control snapshot # @e1 , @e2 , ... browser-control click @e2 browser-control eval 'document.tit
| Stars | 3,141 |
| Forks | 209 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.6308685107076/100 |
| Last Updated | 2026-08-27 |
| Created | 2016-12-21 |
| Platforms | browser, cli, rust |
| Est. Tokens | ~25k |
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browser-control is A tiny, fast Rust CLI that drives a real browser over the Chrome DevTools Protocol — built for coding agents.. It is categorized as a Agent Tool with 3.1k GitHub stars.
browser-control is primarily written in Rust. It covers topics such as agent, browser-automation, llm.
You can find installation instructions and usage details in the browser-control GitHub repository at github.com/keon/browser-control. The project has 3.1k stars and 209 forks, indicating an active community.
browser-control is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to browser-control on Agent Skills Hub include nanobrowser, aichat, code2prompt. 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: