Guides and validates architecture-aware ports of PyTorch/Hugging Face models to Apple MLX, inspects existing local MLX projects, and plans evidence-gated optimizations for Apple Silicon. Use when the user asks to run, port, convert, inspect, quantize, benchmark, or fix a model (LLM, VLM, audio/TTS/ASR, diffusion, SSM, MoE) for MLX, MLX-LM, MLX-VLM, MLX-Audio, or a Mac - e.g. "port this HF model to my Mac", "inspect this MLX app", "run Qwen on Apple Silicon", "convert these safetensors to MLX", "make this faster on my M3", "fix NaN in my MLX port", "speed up prefill / KV cache / speculative decoding", "publish an MLX checkpoint". Also use mid-task when a config.json, safetensors index, weight-shape or tokenizer mismatch, or Metal kernel question appears. Do not use for CUDA-only optimization, non-Apple hardware targets, or general PyTorch/ML questions with no MLX or Apple Silicon connection.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryEngineeringStars0
About this skill
Guides and validates architecture-aware ports of PyTorch/Hugging Face models to Apple MLX, inspects existing local MLX projects, and plans evidence-gated optimizations for Apple Silicon. Use when the user asks to run, port, convert, inspect, quantize, benchmark, or fix a model (LLM, VLM, audio/TTS/ASR, diffusion, SSM, MoE) for MLX, MLX-LM, MLX-VLM, MLX-Audio, or a Mac - e.g. "port this HF model to my Mac", "inspect this MLX app", "run Qwen on Apple Silicon", "convert these safetensors to MLX", "make this faster on my M3", "fix NaN in my MLX port", "speed up prefill / KV cache / speculative decoding", "publish an MLX checkpoint". Also use mid-task when a config.json, safetensors index, weight-shape or tokenizer mismatch, or Metal kernel question appears. Do not use for CUDA-only optimization, non-Apple hardware targets, or general PyTorch/ML questions with no MLX or Apple Silicon connection.
Quick start
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Guides and validates architecture-aware ports of PyTorch/Hugging Face models to Apple MLX, inspects existing local MLX projects, and plans evidence-gated optimizations for Apple Silicon. Use when the user asks to run, port, convert, inspect, quantize, benchmark, or fix a model (LLM, VLM, audio/TTS/ASR, diffusion, SSM, MoE) for MLX, MLX-LM, MLX-VLM, MLX-Audio, or a Mac - e.g. "port this HF model to my Mac", "inspect this MLX app", "run Qwen on Apple Silicon", "convert these safetensors to MLX", "make this faster on my M3", "fix NaN in my MLX port", "speed up prefill / KV cache / speculative decoding", "publish an MLX checkpoint". Also use mid-task when a config.json, safetensors index, weight-shape or tokenizer mismatch, or Metal kernel question appears. Do not use for CUDA-only optimization, non-Apple hardware targets, or general PyTorch/ML questions with no MLX or Apple Silicon connection.
About this listing
Category
Engineering
Reported license
Apache-2.0
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI