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 hongnoul · MCP Server · ★ 115
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is hwatu safe to install? View the security audit →
hwatu Your agents are blind without hwatu hwatu is a visual verification harness for coding agents, built as a WebKit daemon. Instead of "looks right to me", your agent gets one-call verified page checks in 35 ms, pixel-diff scores it can climb, animations as numbers, and headless windows that never steal your focus, at any parallelism. For human-in-the-loop tasks (e.g. Captcha), hwatu features a lightweight visual verification frontend renderer written in WebKit and a caller function. For tiling WMs (Hyprland, sway, niri, i3), hwatu is intended to replace your primary daily browser. Our current goal is to provide scrolling short-form content experience in mobile-level framerate. Documents
| Stars | 115 |
| Forks | 12 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.6068848650707/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-02 |
| Created | 2026-07-18 |
| Platforms | browser, mcp, rust |
| Est. Tokens | ~25k |
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hwatu is Headless verification browser for AI coding agents: 35 ms one-call checks, pixel diffs, live human hand-off. It is categorized as a MCP Server with 115 GitHub stars.
hwatu is primarily written in Rust. It covers topics such as agent-tools, ai-agents, ai-tools.
You can find installation instructions and usage details in the hwatu GitHub repository at github.com/hongnoul/hwatu. The project has 115 stars and 12 forks, indicating an active community.
hwatu is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to hwatu on Agent Skills Hub include safari-mcp, web-agent-protocol, kwin-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: