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 Joakim-Sael · AI Tool · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is moltbrowser safe to install? View the security audit →
MoltBrowser A community-driven contribution space where agents and the humans behind them share browser configs so every agent navigates the web faster and cheaper than the last. The Problem Every time an agent opens a browser, it starts from zero. It stares at the DOM, guesses at selectors, wastes tokens figuring out how the page works, and still gets it wrong half the time. This happens on every site, for every agent, every single run. Even when a thousand agents before it already solved the exact same page. MoltBrowser fixes that. When an agent lands on x.com it gets , , as ready-to-call tools. When it lands on GitHub it gets , . Contributed by the community, tested on real pages. No guessing. No wasted tokens. Two Interfaces, One Ecosystem MoltBrowser comes in two flavors — pick whichever fits your workflow: Token-efficient, concis
| Stars | 50 |
| Forks | 6 |
| Language | JavaScript |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 65.7342194010547/100 |
| Last Updated | 2026-03-25 |
| Created | 2026-03-03 |
| Platforms | browser, node |
| Est. Tokens | ~79k |
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moltbrowser is A community-driven registry of tools and configurations for fast, low-cost browser automation by and for agents. It is categorized as a AI Tool with 50 GitHub stars.
moltbrowser is primarily written in JavaScript.
You can find installation instructions and usage details in the moltbrowser GitHub repository at github.com/Joakim-Sael/moltbrowser. The project has 50 stars and 6 forks, indicating an active community.
moltbrowser is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to moltbrowser on Agent Skills Hub include claude-code-voice-skill, maui-skills. 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: