UltraGameStudio: AI coding agent for game development: engine workflows, gameplay code, and asset generation

by wellingfeng · Codex Skill · ★ 326

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

Security audit verdict: SAFE · quality 61/100

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-agentai-codingasset-generationclaude-codecodexcoding-assistantgame-assetsgame-devgame-developmentgemini

Quick Facts

Stars326
Forks38
LanguageTypeScript
CategoryCodex Skill
LicenseMIT
Quality Score61/100
Open Issues2
Last Updated2026-10-25
Created2026-05-30
Platformsclaude-code, codex, gemini, node
Est. Tokens~19k

About UltraGameStudio

UltraGameStudio The AI coding agent built for game development. In a game engine, code is only a small part of the work. The rest is assets and pipeline — materials, blueprints, terrain, sky, UI, skeletal animation, packaging, performance.

Compatible Skills

These tools work well together with UltraGameStudio for enhanced workflows:

  • UnrealClaude — semantic(0.45)+complementary+rare_topics+similar_pop+shared_platform (60%)
  • ai-forge-mcp — semantic(0.57)+complementary+rare_topics+similar_pop+shared_platform (59%)
  • godot-devtool — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • ai_game_base_analysis_cli_mcp_tool — semantic(0.55)+complementary+rare_topics+similar_pop (58%)

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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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