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Advise on Python-first MLX integration with Swift, C, C++, and non-native language boundaries.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Optimize Python MLX inference and generation loops with warmup, batching, cache handling, synchronization, quantization, and memory checks.
Guide MLX Metal profiling, mx.fast escalation, custom Metal kernels, and C++ extensions when profiling proves kernel-level bottlenecks.
Route Python-first MLX optimization work on Apple Silicon to focused audit, training, inference, Metal, or bridge workflows.
Audit Python MLX repos for lazy-eval, synchronization, compile, dtype, memory, progress, and benchmark issues.
Optimize Python MLX training loops with value_and_grad, accumulation, checkpointing, dtype, validation cadence, memory telemetry, and progress reporting.
| name | mlx-portability-bridges |
| description | Advise on Python-first MLX integration with Swift, C, C++, and non-native language boundaries. |
Use this skill when the user asks how MLX work should cross language or app runtime boundaries.
Before any Python execution, use the target repo's .venv. Never install
Python packages globally.
../../references/portability-bridges.md../../references/profiling-and-metal.mdDo not claim equal-depth MLX optimization support for languages without a first-class MLX API.