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 DY-2026 · Agent Tool · ★ 386
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GameDesignOS safe to install? View the security audit →
GameDesignOS AI-native game design, concept validation, and prototyping system by Paranoia 游戏设计操作系统 / 游思考 GameDesignOS GameDesignOS turns expert game-design methods, evidence, experiments, proposals, and human decisions into reusable skills, stable contracts, and workspace-backed operating procedures. It helps game projects move from idea to validation without losing context between agent calls, documents, and milestones. 简体中文 · English · Runtime Foundation · Workspace · Workflows · Roadmap · Skills · Cases · License <img alt="Domain" src="https://img.shie
| Stars | 386 |
| Forks | 46 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 61.741184900182/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-17 |
| Created | 2026-05-23 |
| Platforms | python |
| Est. Tokens | ~20k |
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GameDesignOS is Local-first game design OS for AI agents: turn sessions into evidence, experiments, reviewable decisions, and durable project memory—Human Gates and rollback.. It is categorized as a Agent Tool with 386 GitHub stars.
GameDesignOS is primarily written in Python. It covers topics such as agent-skills, ai-agents, decision-support.
You can find installation instructions and usage details in the GameDesignOS GitHub repository at github.com/DY-2026/GameDesignOS. The project has 386 stars and 46 forks, indicating an active community.
GameDesignOS is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to GameDesignOS on Agent Skills Hub include OpenAdapt, novel-to-game, novel-to-game. 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: