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 · ★ 170
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ParanoiaSkills safe to install? View the security audit →
ParanoiaSkills A verifiable agent-skill operating system for game-design production and AI workflow governance. It turns evidence into diagnosis, concepts into validation contracts, production notes into reviewable proposals, ED problems into weekly experiments, and workflow changes into eval-backed evolution. 简体中文 · English · What This Is · Try in 10 Minutes · Easy Start · 60-Second Demo · Case · Current Skills · Showcase · Star History · License <img alt="Method" src="https://img.sh
| Stars | 170 |
| Forks | 25 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.571159990689/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-17 |
| Created | 2026-05-23 |
| Platforms | python |
| Est. Tokens | ~19k |
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ParanoiaSkills is Evidence-first agent skills for game design analysis, concept architecture, and AI workflow evolution.. It is categorized as a Agent Tool with 170 GitHub stars.
ParanoiaSkills is primarily written in Python. It covers topics such as agent-skills, game-analysis, game-design.
You can find installation instructions and usage details in the ParanoiaSkills GitHub repository at github.com/DY-2026/ParanoiaSkills. The project has 170 stars and 25 forks, indicating an active community.
ParanoiaSkills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ParanoiaSkills on Agent Skills Hub include novel-to-game, novel-to-game, gstack-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: