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 jmanhype · MCP Server · ★ 92
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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vggt-mps Port of Facebook Research's VGGT (Visual Geometry Grounded Transformer) to Apple Silicon via PyTorch's MPS backend. Takes single or multi-view images and produces depth maps, camera poses, and 3D point clouds. What it produces Given N input images, VGGT predicts: Architecture The upstream VGGT model is a 1B-parameter transformer trained on multi-view geometry tasks. This repo wraps it with: MPS device detection and dtype handling (float32 for Metal compatibility) A sparse attention module () that patches the model at runtime for O(n) memory scaling instead of O(n^2) A unified CLI ( command with subcommands) A Gradio web interface An MCP server for Claude Desktop integration vggt-mps/ src/ vggtcore.py # Core VGGT processing vggtsparseattention.py # Runtime sparse attention patch config.py # Centralized configuration visualization.py #
| Stars | 92 |
| Forks | 10 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.2040328080351/100 |
| Open Issues | 8 |
| Last Updated | 2026-03-25 |
| Created | 2025-09-18 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~2539k |
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vggt-mps is VGGT 3D Vision Agent optimized for Apple Silicon with Metal Performance Shaders. It is categorized as a MCP Server with 92 GitHub stars.
vggt-mps is primarily written in Python. It covers topics such as 3d-reconstruction, apple-silicon, claude-desktop.
You can find installation instructions and usage details in the vggt-mps GitHub repository at github.com/jmanhype/vggt-mps. The project has 92 stars and 10 forks, indicating an active community.
vggt-mps is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to vggt-mps on Agent Skills Hub include daymon, safari-mcp, CoWork-OS. 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: