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 kelos-dev · Codex Skill · ★ 331
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is kelos safe to install? View the security audit →
Kelos Orchestrate autonomous AI coding agents on Kubernetes. Quick Start · Kelos Skill · Examples · Integration · Reference · YAML Manifests Kelos is a Kubernetes-native framework for AI coding agents. It does two things: Defines the agent and the environment it runs in as one unit — the prompt, model, instructions, plugins, MCP servers, git workspace, credentials, and Pod resources all live together as Kubernetes resources you can version-control.
| Stars | 331 |
| Forks | 40 |
| Language | Go |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 67.3578560801554/100 |
| Open Issues | 146 |
| Last Updated | 2026-09-19 |
| Created | 2026-02-01 |
| Platforms | claude-code, codex, gemini, go, k8s |
| Est. Tokens | ~21k |
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kelos is Kelos - The Kubernetes-native framework for orchestrating autonomous AI coding agents.. It is categorized as a Codex Skill with 331 GitHub stars.
kelos is primarily written in Go. It covers topics such as agentic-ai, agentic-coding, ai.
You can find installation instructions and usage details in the kelos GitHub repository at github.com/kelos-dev/kelos. The project has 331 stars and 40 forks, indicating an active community.
kelos is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to kelos on Agent Skills Hub include palot, OpenContext, Mysti. 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.
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