When should I use this skill?

Use it at the start of sessions involving intensive builds, GPU-based training, or when you need to avoid exceeding cloud compute budgets.

What does the CPU/GPU Performance skill do?

It provides Claude with specialized commands and logic to monitor, profile, and optimize hardware resource usage during heavy tasks like compilation or model training.

Can it help prevent GPU memory errors?

Yes, it includes tools for monitoring GPU memory utilization and suggests strategies like gradient accumulation or smaller batch sizes to prevent memory eviction.

Does it support standard profiling tools?

Yes, it integrates with standard system tools like perf, nvidia-smi, nsys, and language-specific profilers to identify and log performance bottlenecks.

How does this skill help reduce development costs?

By establishing per-task budgets and favoring selective testing over 'whole world' runs, it reduces unnecessary compute cycles and cluster hours.

CPU & GPU Performance Optimizer

Name: CPU & GPU Performance Optimizer
Author: athola

byathola

•

130

Analytics & Monitoring

Monitors and optimizes CPU and GPU resource consumption during intensive development, builds, and training tasks.

About

The CPU & GPU Performance skill provides Claude with a disciplined framework for managing hardware resources during compute-intensive operations. By establishing baselines with tools like nvidia-smi and uptime, narrowing execution scopes for testing, and implementing workload throttling, it prevents system starvation and keeps cloud compute costs under control. It is ideal for developers working on machine learning models, large-scale compilations, or resource-heavy test suites where proactive monitoring and cost-effective validation are essential.

Key Features

Workload throttling and sequencing to prevent system starvation
Scope narrowing strategies for selective testing and targeted builds
Automated performance baseline capture for CPU and GPU hosts
130 GitHub stars
Comprehensive instrumentation with profilers like nsys and flamegraph
Resource cost logging for tracking CI/cluster budget consumption

Use Cases

Narrowing test suite execution to avoid unnecessary compute on small fixes
Optimizing resource-heavy machine learning model training and evaluation
Managing CPU-intensive build processes for large-scale software projects

About

Key Features

Workload throttling and sequencing to prevent system starvation
Scope narrowing strategies for selective testing and targeted builds
Automated performance baseline capture for CPU and GPU hosts
130 GitHub stars
Comprehensive instrumentation with profilers like nsys and flamegraph
Resource cost logging for tracking CI/cluster budget consumption

Use Cases

Narrowing test suite execution to avoid unnecessary compute on small fixes
Optimizing resource-heavy machine learning model training and evaluation
Managing CPU-intensive build processes for large-scale software projects