mlx-optimizer

name	mlx-optimizer
description	Route Python-first MLX optimization work on Apple Silicon to focused audit, training, inference, Metal, or bridge workflows.

MLX Optimizer

Use this skill when the user asks Codex to optimize, audit, benchmark, profile, or explain MLX code on Apple Silicon.

First Actions

1. Identify the target repo or file.
2. Check for a repo-local .venv before any Python execution.
3. Inspect project structure before proposing new modules.
4. Treat static observations as candidate findings until verified.
5. Require concrete evidence for optimization claims.
6. For version-sensitive MLX API behavior, retrieve current authoritative MLX docs before implementation or recommendations.

Routing

• Repo-wide or unknown performance issue: read ../mlx-performance-audit/SKILL.md.
• Training loop issue: read ../mlx-training-optimizer/SKILL.md.
• Inference or generation issue: read ../mlx-inference-optimizer/SKILL.md.
• Metal capture, custom kernel, or C++ extension question: read ../mlx-metal-kernels/SKILL.md.
• Swift/C/C++ or other language integration: read ../mlx-portability-bridges/SKILL.md.

Core References

Read only the files needed for the routed task:

• ../../references/mlx-core-concepts.md
• ../../references/eval-and-synchronization.md
• ../../references/compile-and-transforms.md
• ../../references/memory-and-dtypes.md
• ../../references/reporting-format.md

Non-Negotiables

• Never install Python packages globally.
• Preserve unrelated user changes.
• Prefer existing repo abstractions.
• Do not recommend automatic rewrites without active code-path evidence.
• Include commands, outcomes, and residual risks in the final handoff.