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system-profile

Profile Python scripts, processes, GPU workloads, and memory to generate performance reports.

1|Updated Mar 26, 2026
Search Tags:#performance#python#profiling#instrumentation#benchmark#gpu#profile
One-click install
npx skills add https://github.com/Lingrongye/federated-learning --skill system-profile
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: system-profile
Source: https://github.com/Lingrongye/federated-learning/tree/main/Auto-claude-code-research-in-sleep/skills/system-profile
Command: npx skills add https://github.com/Lingrongye/federated-learning --skill system-profile

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profile targets (scripts, processes, GPU, memory, interconnect) to identify bottlenecks and generate actionable performance reports.

Core Features & Use Cases

  • Instrumentation-driven profiling across CPU, memory, GPU, and interconnect, with automated guidance on bottlenecks and optimization steps.
  • Supports selective targets (e.g., training scripts, serving processes) and end-to-end profiling with actionable recommendations.
  • Produces structured reports you can use to prioritize fixes and improvements.

Quick Start

Specify the target to profile (e.g., train.py, a running process, or a GPU workload) and run the profiling workflow to generate a report.

Frequently Asked Questions about system-profile

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile a Python script to identify performance bottlenecks?▼

To profile a Python script, specify your target file like train.py and run the profiling workflow to instrument the code, identify bottlenecks, and generate an actionable report saved to a profile_output directory.

Can I profile GPU workloads and memory usage for running processes?▼

Yes, you can profile GPU workloads, memory, and interconnect usage by targeting running processes. The profiling workflow instruments the active process to identify specific hardware bottlenecks and outputs structured optimization reports.

What is the best way to instrument code for benchmarking without manual overhead?▼

The best way to benchmark without manual overhead is using automated instrumentation-driven profiling. It selectively targets scripts or processes to capture performance metrics and produces structured reports to prioritize fixes.

Does this profiling workflow require specific dependencies to generate a report?▼

No specific dependencies are required to run the profiling workflow. You only need to specify a target like a training script or GPU workload, and it automatically handles instrumentation to generate the performance report.

When should I not use automated profiling for optimization?▼

You should not use automated profiling when your target lacks clear CPU, memory, GPU, or interconnect bottlenecks, as the workflow is designed to instrument and identify hardware and software performance limits for actionable optimization.

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