by riccardomusmeci · Agent Tool · ★ 468
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pip install mlx-llmTaken from the project's README on GitHub.
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.
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.
| Stars | 468 |
| Forks | 27 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 63/100 |
| Open Issues | 1 |
| Last Updated | 2025-01-29 |
| Created | 2023-12-07 |
| Platforms | python |
| Est. Tokens | ~127k |
mlx-llm Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX. Go to the entire Youtube Video. How to install 🔨 Models 🧠 Currently, out-of-the-box supported models are: To create a model with pre-trained weights from HuggingFace: You can also load a new version of pre-trained weights for a
These tools work well together with mlx-llm for enhanced workflows:
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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: