happyclaw — security grade SAFE, quality 53/100

Security audit verdict: SAFE · quality 53/100

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 riba2534 · Codex Skill · ★ 805

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is happyclaw safe to install? View the security audit →

About happyclaw

HappyClaw 自托管的多用户本地 AI Agent 系统 — Powered By Claude Code. 介绍 · 核心能力 · 快速开始 · 技术架构 · 贡献 更多截图 设置向导 | <img src="docs/screenshots/setu

openclaw

Quick Facts

Stars805
Forks156
LanguageTypeScript
CategoryCodex Skill
LicenseMIT
Quality Score53.2706084355154/100
Open Issues4
Last Updated2026-08-18
Created2026-02-14
Platformsnode
Est. Tokens~20k

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Frequently Asked Questions

What is happyclaw?

happyclaw is happy happy happyclaw~. It is categorized as a Codex Skill with 805 GitHub stars.

What programming language is happyclaw written in?

happyclaw is primarily written in TypeScript. It covers topics such as openclaw.

How do I install or use happyclaw?

You can find installation instructions and usage details in the happyclaw GitHub repository at github.com/riba2534/happyclaw. The project has 805 stars and 156 forks, indicating an active community.

What license does happyclaw use?

happyclaw is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to happyclaw?

The top alternatives to happyclaw on Agent Skills Hub include antigravity-claude-proxy, buildwithclaude, skillshare. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools