openclaw-operator: Kubernetes operator for deploying and managing OpenClaw AI agent instances with production-grade security

by openclaw-rocks · Codex Skill · ★ 360

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

Security audit verdict: SAFE · quality 75/100

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.

agentsaigolanghelmkubernetesopenclawoperator

Quick Facts

Stars360
Forks64
LanguageGo
CategoryCodex Skill
LicenseApache-2.0
Quality Score75/100
Open Issues1
Last Updated2026-05-23
Created2026-02-06
Platformsgo, k8s
Est. Tokens~300k

About openclaw-operator

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.

Compatible Skills

These tools work well together with openclaw-operator for enhanced workflows:

  • kubernetes-mcp-server — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (64%)
  • k8s-mcp-server — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • kelos — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • mcp-k8s — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

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:

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