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 prajwalshettydev · MCP Server · ★ 632
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is UnrealGenAISupport safe to install? View the security audit →
Usage Examples: MCP Example: Claude spawning scene objects and controlling their transformations and materials, generating blueprints, functions, variables, adding components, running python scripts etc. API Example: A project called become human, where NPCs are OpenAI agentic instances. Built using this plugin. Unreal Engine Generative AI Support Plugin: Every month, hundreds of new AI models are released by various organizations, making it hard to kee
| Stars | 632 |
| Forks | 100 |
| Language | C++ |
| Category | MCP Server |
| License | MIT |
| Quality Score | 62.605927469672/100 |
| Open Issues | 9 |
| Last Updated | 2026-04-28 |
| Created | 2024-11-17 |
| Platforms | claude-code, gemini, mcp |
| Est. Tokens | ~1239k |
These tools work well together with UnrealGenAISupport for enhanced workflows:
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UnrealGenAISupport is Unreal Engine plugin for LLM/GenAI models & MCP UE5 server. OpenAI GPT-5, Deepseek R1, Claude Opus/Sonnet, Gemini 3, Grok 4, Alibaba Qwen, Kimi, ElevenLabs TTS, Inworld, OpenRouter, Groq, GLM, Ollama,. It is categorized as a MCP Server with 632 GitHub stars.
UnrealGenAISupport is primarily written in C++. It covers topics such as claude, code, dashscope.
You can find installation instructions and usage details in the UnrealGenAISupport GitHub repository at github.com/prajwalshettydev/UnrealGenAISupport. The project has 632 stars and 100 forks, indicating an active community.
UnrealGenAISupport is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to UnrealGenAISupport on Agent Skills Hub include py-gpt, UnrealClaude, Overture. 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: