by adversa-ai · Codex Skill · ★ 349
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/adversa-ai/secureclaw.git
npm install @adversa/secureclawTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 349 |
| Forks | 47 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 65/100 |
| Open Issues | 6 |
| Last Updated | 2026-04-12 |
| Created | 2026-02-10 |
| Platforms | node |
| Est. Tokens | ~55k |
SecureClaw End-to-End security platform for OpenClaw AI agents. Audit, Hardening and Runtime Security for OpenClaw. Developed by Adversa AI -- Agentic AI Security and Red Teaming Pioneers. 56 audit checks. 15 behavioral rules. 9 scripts. 4 pattern databases. 7 security frameworks mapped.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: