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 ArmorerLabs · Codex Skill · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Armorer safe to install? View the security audit →
Armorer Local control plane for AI agents Run OpenClaw, NanoClaw, and future agents with local sandboxes, guided setup, credential handling, guardrails, approvals, jobs, logs, and runtime health in one place. Run any agent. Securely. Local-first by default. Experimental release candidate: Armorer is under active development. The current release train is intended for early testers who are comfortable with local agent runtimes, Docker/Colima, and rapidly evolving setup flows. Website · Install · Docs for humans · [Issues](https://github.com/Armor
| Stars | 60 |
| Forks | 3 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 63.2631773617152/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-01 |
| Created | 2026-02-06 |
| Platforms | docker, node |
| Est. Tokens | ~15k |
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Armorer is Local control plane for running AI agents with sandboxes, approvals, guardrails, credentials, and runtime health.. It is categorized as a Codex Skill with 60 GitHub stars.
Armorer is primarily written in TypeScript. It covers topics such as agent-runtime, agent-security, ai-agents.
You can find installation instructions and usage details in the Armorer GitHub repository at github.com/ArmorerLabs/Armorer. The project has 60 stars and 3 forks, indicating an active community.
Armorer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Armorer on Agent Skills Hub include ClawGuard, agent-security-scanner-mcp, rampart. 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: