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system-profile

Profile CPU, memory, GPU, and interconnect bottlenecks and generate structured performance reports.

Updated Apr 1, 2026
Search Tags:#performance#memory#profiling#instrumentation#gpu#cpu
One-click install
npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill system-profile-wenwen555
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: system-profile
Source: https://github.com/Wenwen555/ARIS-LVLM/tree/main/skills/system-profile
Command: npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill system-profile-wenwen555

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiling target systems to identify bottlenecks across CPU, memory, GPU, and interconnect, and to produce structured, actionable performance reports.

Core Features & Use Cases

  • Profile Python scripts, running processes, end-to-end ML pipelines, or service workloads.
  • Automatically insert instrumentation where needed and generate a concise bottleneck-focused report.
  • Suitable for debugging training loops, inference servers, and data processing pipelines.

Quick Start

Profile the target (e.g., "train.py" or "vllm serving") to generate a performance report and recommended instrumentation changes.

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 find CPU and memory bottlenecks?▼

To profile a Python script for CPU and memory bottlenecks, you can run the target through this Skill to automatically collect performance data and generate a structured report with actionable recommendations.

Can I profile a running process for GPU and interconnect performance issues?▼

Yes, you can profile running processes to identify GPU and interconnect performance issues, as the Skill supports targeting active workloads and outputs a bottleneck-focused final report.

How does performance profiling work for end-to-end ML workflows?▼

Performance profiling for end-to-end ML workflows works by using a mix of external tools and optional instrumentation to collect data across CPU, memory, GPU, and interconnect dimensions, outputting a final report and changelog.

What is the best way to debug performance bottlenecks in inference servers?▼

The best way to debug inference server bottlenecks is to profile the service workload to uncover CPU, memory, GPU, and interconnect limitations, producing structured recommendations and an instrumentation changelog.

Do I need to manually add instrumentation to profile training loops?▼

No, you do not need to manually add instrumentation to profile training loops, as the Skill automatically inserts instrumentation where needed and generates a concise bottleneck-focused report.

What limitations exist when profiling service deployments across multiple dimensions?▼

A known limitation is that profiling service deployments relies on a mix of external tools and optional instrumentation, which may require environment-specific configurations to accurately capture CPU, memory, GPU, and interconnect data.

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