meshgen: Use AI Agents directly in Blender

by huggingface · Agent Tool · ★ 943

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

Featured in: Blender, Unity & Godot MCP

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.

Quick Facts

Stars943
Forks83
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score56/100
Open Issues20
Last Updated2026-10-22
Created2024-10-14
Platformspython
Est. Tokens~14k

About meshgen

MeshGen Use AI Agents to control Blender with natural language. Features Focused on AI as a tool, not replacement Multiple backend options: Local inference with llama.cpp or Ollama Remote inference with Hugging Face, Anthropic, or OpenAI Optional LLaMA-Mesh integration for local mesh understanding and generation Optional Hyper3D integration for high-fidelity 3D mesh generation Installation Go to...

Compatible Skills

These tools work well together with meshgen for enhanced workflows:

  • blender-ai-mcp-addon — semantic(0.44)+complementary+shared_fw(ollama,openai)+same_lang+similar_pop+shared_platform (82%)
  • mcp-for-blender-pro — semantic(0.42)+complementary+shared_fw(ollama,openai)+same_lang+similar_pop+shared_platform (81%)
  • GenesisCore — semantic(0.39)+complementary+shared_fw(anthropic,openai)+same_lang+similar_pop+shared_platform (80%)
  • blender-ai-mcp-addon — semantic(0.43)+complementary+shared_fw(ollama)+same_lang+similar_pop+shared_platform (73%)
  • Auto-Use — semantic(0.19)+complementary+shared_fw(anthropic,openai)+same_lang+similar_pop+shared_platform (73%)

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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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