Does it support profiling for specific programming languages?

Yes, the skill integrates with various language-specific tools such as cargo flamegraph for Rust, intel vtune, and framework-specific timeline tracers.

How does the throttling feature work?

It leverages system-level tools like nice, ionice, and Kubernetes/Slurm quotas to sequence heavy tasks and prevent the starvation of shared nodes.

What metrics are captured during the baseline phase?

The skill captures current system uptime, CPU load averages via ps, and GPU memory usage to establish a clear resource starting point before work begins.

How does this skill help reduce cloud compute costs?

It implements strict resource budgeting and selective testing strategies, ensuring you only run the necessary code paths instead of full test suites or redundant training runs.

Can I use this for GPU-intensive tasks like deep learning?

Yes, it includes specific instrumentation for NVIDIA GPUs using nvidia-smi and nsys to monitor memory footprint, kernel utilization, and data pipeline latency.

Performance & Resource Monitoring

Name: Performance & Resource Monitoring
Author: athola

byathola

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Analíticas y Monitorización

Monitors and optimizes CPU and GPU utilization to prevent resource exhaustion and manage computational costs during development.

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The Performance & Resource Monitoring skill provides a structured framework for managing hardware resources within Claude Code. It enables developers to establish performance baselines, instrument code with professional profilers like perf or nvidia-smi, and implement throttling strategies to avoid over-utilizing shared infrastructure. By emphasizing selective testing and disciplined resource logging, this skill ensures that heavy computational tasks like model training or complex builds are handled efficiently and within defined budgetary constraints.

Características Principales

Selective testing and scope reduction to minimize redundant computations
Real-time CPU and GPU utilization tracking and baselining
Advanced profiling integration for CPU and GPU workloads
Detailed resource logging for cost-effective validation and audit trails
Resource throttling and task sequencing to prevent system starvation
10 GitHub stars

Casos de Uso

Debugging performance bottlenecks in data pipelines by capturing kernel and I/O timelines
Preventing expensive GPU overruns during machine learning model training and evaluation
Optimizing high-concurrency build processes to avoid pinning all available CPU cores

Acerca de

Características Principales

Selective testing and scope reduction to minimize redundant computations
Real-time CPU and GPU utilization tracking and baselining
Advanced profiling integration for CPU and GPU workloads
Detailed resource logging for cost-effective validation and audit trails
Resource throttling and task sequencing to prevent system starvation
10 GitHub stars

Casos de Uso

Debugging performance bottlenecks in data pipelines by capturing kernel and I/O timelines
Preventing expensive GPU overruns during machine learning model training and evaluation
Optimizing high-concurrency build processes to avoid pinning all available CPU cores