一键在 Manus 中运行任何 Skill

开始使用

mlx-performance-audit

星标0

分支0

更新时间2026年6月12日 11:33

Audit Python MLX repos for lazy-eval, synchronization, compile, dtype, memory, progress, and benchmark issues.

安装

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。

在 Manus 中运行

来源

sealad886

sealad886/mlx-optimizer-plugin

打开 GitHub 仓库查看创作者相关仓库

下载

在 Manus 中运行

MLX Performance Audit

Use this skill for repo-wide MLX performance reviews and unknown bottlenecks.

Python Environment

Before any Python execution, use the target repo's .venv. Never install Python packages globally.

Required References

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

Workflow

Inspect repo state with git --no-pager status --short.
Locate dependency files, MLX imports, training loops, inference loops, dataloaders, benchmark scripts, and progress-reporting patterns.
Run ../../scripts/mlx_audit.py from this plugin repo when the target repo is local and scanning is useful.
Keep candidate findings separate from verified findings.
Recommend measurement before changes: warmup, synchronization, repeated runs, memory telemetry, correctness checks, and representative input sizes.

Report Shape

Use ../../templates/optimization-report.md as the report structure. Every finding needs file, line, evidence, candidate impact, confidence, verification, and residual risk.

同仓库更多 Skills

同仓库

mlx-inference-optimizer

sealad886/mlx-optimizer-plugin

Optimize Python MLX inference and generation loops with warmup, batching, cache handling, synchronization, quantization, and memory checks.

2026-06-120

mlx-metal-kernels

sealad886/mlx-optimizer-plugin

Guide MLX Metal profiling, mx.fast escalation, custom Metal kernels, and C++ extensions when profiling proves kernel-level bottlenecks.

2026-06-120

mlx-optimizer

sealad886/mlx-optimizer-plugin

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

2026-06-120

mlx-portability-bridges

sealad886/mlx-optimizer-plugin

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

2026-06-120

mlx-training-optimizer

sealad886/mlx-optimizer-plugin

Optimize Python MLX training loops with value_and_grad, accumulation, checkpointing, dtype, validation cadence, memory telemetry, and progress reporting.

2026-06-120

name	mlx-performance-audit
description	Audit Python MLX repos for lazy-eval, synchronization, compile, dtype, memory, progress, and benchmark issues.

MLX Performance Audit

Use this skill for repo-wide MLX performance reviews and unknown bottlenecks.

Python Environment

Before any Python execution, use the target repo's .venv. Never install Python packages globally.

Required References

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

Workflow

Inspect repo state with git --no-pager status --short.
Locate dependency files, MLX imports, training loops, inference loops, dataloaders, benchmark scripts, and progress-reporting patterns.
Run ../../scripts/mlx_audit.py from this plugin repo when the target repo is local and scanning is useful.
Keep candidate findings separate from verified findings.
Recommend measurement before changes: warmup, synchronization, repeated runs, memory telemetry, correctness checks, and representative input sizes.

Report Shape

Use ../../templates/optimization-report.md as the report structure. Every finding needs file, line, evidence, candidate impact, confidence, verification, and residual risk.