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 ttguy0707 · Codex Skill · ★ 329
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CyberClaw safe to install? View the security audit →
CyberClaw 当 AI 开始"黑箱操作"，你需要一双透视眼 下一代透明智能体架构 · Next-Gen Transparent Agent Architecture 快速开始 · 核心能力 · 架构图 · 示例 🤖 你的 AI 在背着你做什么？CyberClaw 让所有行为无所遁形 💡 灵感来源：受 OpenClaw 的启发，CyberClaw 专注于解决 AI 智能体的透明度和可控性问题。 📖 简介 CyberClaw 是一个企业级透明可控智能体，重新定义 AI 系统的可信边界： 🔍 白盒化决策 → 5 类事件审计 + JSONL 日志 + Rich 监控终端，所有行为可追溯 🛡️ 零信任执行 → 两段式调用（help → run），先看说明书再执行，P0 级事故率降低 80% 🧠 持续学习 → 双水位记忆系统（长期画像 + 短期摘要），越用越懂你 ⚡ 复杂任务编排 → 心跳任务系统 + 可插拔技能 + MCP 服务集成，解放双手 🔌 技能生态兼容 CyberClaw 支持OpenClaw 技能和Claude Code 技能，可直接使用两个生态系统的丰富技能资源，无需重新开发。 🌟 核心能力 | 🧠 双水
| Stars | 329 |
| Forks | 34 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.7847236009202/100 |
| Last Updated | 2026-09-10 |
| Created | 2026-04-07 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
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CyberClaw is 👾 下一代透明智能体架构 | Next-Gen Transparent Agent Architecture 🔍 全行为审计 | 🛡️ 两段式安全调用 | 🧠 双水位记忆 | ⏰ 心跳任务 📊 P0 级事故率降低 80% | 兼容 OpenClaw + Claude Code 技能生态. It is categorized as a Codex Skill with 329 GitHub stars.
CyberClaw is primarily written in Python. It covers topics such as agent, agent-framework, ai-agent.
You can find installation instructions and usage details in the CyberClaw GitHub repository at github.com/ttguy0707/CyberClaw. The project has 329 stars and 34 forks, indicating an active community.
CyberClaw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to CyberClaw on Agent Skills Hub include ai-agent-tools-catalog, fullstack-langgraph-nextjs-agent, LightAgent. 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: