Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by kivo360 · MCP Server · ★ 78
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OmoiOS safe to install? View the security audit →
Open-source orchestration runtime that turns specs into PRs using parallel agent swarms in isolated sandboxes. If you find OmoiOS useful, consider giving it a star — it helps others discover the project. The Problem AI coding agents are powerful individually, but using them at scale is a mess. You paste a prompt, wait, review, paste another prompt, fix wha
| Stars | 78 |
| Forks | 7 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 64.2603667833638/100 |
| Open Issues | 17 |
| Last Updated | 2026-06-18 |
| Created | 2025-11-16 |
| Platforms | claude-code, codex, gemini, mcp, python |
| Est. Tokens | ~18k |
These tools work well together with OmoiOS for enhanced workflows:
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OmoiOS is Turn feature specs into merged PRs with a self-supervising swarm of coding agents — parallel execution, isolated sandboxes, DAG dependencies. Open-source, self-hostable, model-agnostic (Claude / Gemin. It is categorized as a MCP Server with 78 GitHub stars.
OmoiOS is primarily written in Python. It covers topics such as agent-framework, ai-agents, ai-developer-tools.
You can find installation instructions and usage details in the OmoiOS GitHub repository at github.com/kivo360/OmoiOS. The project has 78 stars and 7 forks, indicating an active community.
OmoiOS is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to OmoiOS on Agent Skills Hub include genie, intellegix-code-agent-toolkit, orchestkit. 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: