ai-game-devtools: Your AI Game Dev Hub

by Yuan-ManX · Agent Tool · ★ 1.3k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 56/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsai-game-developmentai-game-engineai-platformai-toolkitaigcartificial-intelligenceawesome-listdeep-learninggame-ai

Quick Facts

Stars1,346
Forks130
LanguageJavaScript
CategoryAgent Tool
LicenseMIT
Quality Score56/100
Open Issues13
Last Updated2026-07-21
Created2023-03-21
Platformsnode
Est. Tokens~25k

About ai-game-devtools

AI Game DevTools (AI-GDT) 🎮 Your AI Game Dev Hub. 🚀 The ultimate resource hub for AI-powered game development tools. Discover cutting-edge LLMs, World Model, Agent, Code, Image, Texture, Shader, 3D Model, Animation, Video, Audio, Music, Singing Voice and Analytics.

Compatible Skills

These tools work well together with ai-game-devtools for enhanced workflows:

  • Unity-MCP — semantic(0.30)+complementary+rare_topics+similar_pop (54%)
  • unity-mcp — semantic(0.21)+complementary+rare_topics+similar_pop (51%)
  • UE5-MCP — semantic(0.27)+complementary+rare_topics+similar_pop (49%)
  • mcp-unity — semantic(0.21)+complementary+rare_topics+similar_pop (46%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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