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 VibePod · Codex Skill · ★ 158
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is vibepod-cli safe to install? View the security audit →
VibePod VibePod is a unified CLI () for running AI coding agents in isolated Docker containers — no required configuration, no setup. Just . Includes built-in local metrics collection, HTTP traffic tracking, and an analytics dashboard to monitor and compare agents side-by-side. Features ⚡ Zero config — no setup required; just works. Optional YAML for custom configuration 🐳 Isolated agents — each agent runs in its own Docker container 🔀 Unified interface — one CLI for Claude, Gemini, Codex, Devstral/Vibe, Copilot, Auggie, Pi & more 🧩 Skills
| Stars | 158 |
| Forks | 8 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 75.7363254142498/100 |
| Open Issues | 22 |
| Last Updated | 2026-09-12 |
| Created | 2026-02-10 |
| Platforms | claude-code, cli, codex, docker, python |
| Est. Tokens | ~15k |
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vibepod-cli is Unified CLI for running AI coding agents in isolated containers. Includes built-in local metrics collection, HTTP traffic tracking, and an analytics dashboard to track agent actions.. It is categorized as a Codex Skill with 158 GitHub stars.
vibepod-cli is primarily written in Python. It covers topics such as agentic-ai, ai-agents, ai-coding-assistant.
You can find installation instructions and usage details in the vibepod-cli GitHub repository at github.com/VibePod/vibepod-cli. The project has 158 stars and 8 forks, indicating an active community.
vibepod-cli is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to vibepod-cli on Agent Skills Hub include code-on-incus, Codeman, clampdown. 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: