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 openclaw-rocks · Codex Skill · ★ 360
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-operator safe to install? View the security audit →
OpenClaw Kubernetes Operator Self-host OpenClaw AI agents on Kubernetes with production-grade security, observability, and lifecycle management. OpenClaw is an AI agent platform that acts on your behalf across Telegram, Discord, WhatsApp, and Signal. It manages your inbox, calendar, smart home, and more through 50+ integrations. While OpenClaw.rocks offers fully managed hosting, this operator lets you run OpenClaw on your own infrastructure with the same operational rigor. Why an Operator? Deploying AI agents to Kubernetes involves more than a Deployment and a Service. You need network iso
| Stars | 360 |
| Forks | 64 |
| Language | Go |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 74.6830283335226/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-23 |
| Created | 2026-02-06 |
| Platforms | go, k8s |
| Est. Tokens | ~300k |
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openclaw-operator is Kubernetes operator for deploying and managing OpenClaw AI agent instances with production-grade security, observability, and lifecycle management.. It is categorized as a Codex Skill with 360 GitHub stars.
openclaw-operator is primarily written in Go. It covers topics such as agents, ai, golang.
You can find installation instructions and usage details in the openclaw-operator GitHub repository at github.com/openclaw-rocks/openclaw-operator. The project has 360 stars and 64 forks, indicating an active community.
openclaw-operator is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-operator on Agent Skills Hub include openclaw-operator, mariadb-operator, context-space. 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: