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 lightningpixel · Agent Tool · ★ 7.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is modly safe to install? View the security audit →
Modly Local, open source, AI-powered image-to-3D mesh generation. Turn any photo into a 3D model using open source AI models running entirely on your GPU. Modly is a desktop application for Windows and Linux (macOS coming soon) Created by Lightning Pixel Download Head to the Releases page to download the latest installer for Windows or Linux. Alternatively, you can clone the repository and run the app directly without installing: Getting started Install JS dependencies Set up Python backend Run in development Extension system Modly supports external AI model extensions. Each extension is a GitHub repository containing a and a . Official extensions https://github.com/lightningpixel/modly-hunyuan3d-mini-ex
| Stars | 7,919 |
| Forks | 738 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 67.1244440369398/100 |
| Open Issues | 109 |
| Last Updated | 2026-10-02 |
| Created | 2026-03-17 |
| Platforms | node |
| Est. Tokens | ~14k |
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modly is Desktop app to generate 3D models from images or prompt using local AI — runs entirely on your GPU. It is categorized as a Agent Tool with 7.9k GitHub stars.
modly is primarily written in TypeScript. It covers topics such as 3d, ai-local, ai-tools.
You can find installation instructions and usage details in the modly GitHub repository at github.com/lightningpixel/modly. The project has 7.9k stars and 738 forks, indicating an active community.
The top alternatives to modly on Agent Skills Hub include autoclip, clawpanel, open-cowork. 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: