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Advise on Python-first MLX integration with Swift, C, C++, and non-native language boundaries.
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Based on SOC occupation classification
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.