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mlx-performance-audit

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Atualizado12 de junho de 2026 às 11:33

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

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Fonte

sealad886

sealad886/mlx-optimizer-plugin

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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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.

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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.