skills.rest
scottgl9
scottgl9COMMUNITY
Explore all skills by this author →
WebsiteGitHub

generate-profile

Launch, validate, and profile SGLang server instances with Chrome and Perfetto traces.

12|2|Updated Mar 22, 2026
Search Tags:#performance#optimization#profiling#trace#server#performance-analysis
One-click install
npx skills add https://github.com/scottgl9/sglang-spark-gb10-optimizations --skill generate-profile-scottgl9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-profile
Source: https://github.com/scottgl9/sglang-spark-gb10-optimizations/tree/main/.claude/skills/generate-profile
Command: npx skills add https://github.com/scottgl9/sglang-spark-gb10-optimizations --skill generate-profile-scottgl9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of profiling and analyzing SGLang server performance, helping users visualize execution and optimize models efficiently.

Core Features & Use Cases

  • Launch and monitor SGLang servers to ensure readiness and correct setup.
  • Validate model accuracy with quick evaluation scripts before profiling.
  • Generate detailed profiling traces for performance optimization, compatible with Chrome and Perfetto.
  • Use case: Quickly profiling a running large language model server to identify bottlenecks and improve inference latency for deployment.

Quick Start

Launch the server, check health, run accuracy validation, generate a trace, and review the profiling output.

Frequently Asked Questions about generate-profile

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

FAQPage Schema
How do I profile an SGLang server to analyze inference performance?▼

You can profile an SGLang server by automating its launch, validating model accuracy, and generating detailed execution traces. These traces are compatible with Chrome and Perfetto to help you analyze performance metrics and optimize inference latency.

What tools can I use to visualize SGLang profiling traces?▼

You can visualize SGLang profiling traces using Chrome and Perfetto. These compatible tools allow you to inspect generated execution workflows, identify performance bottlenecks, and tune large language model inference latency.

Do I need Python to run SGLang server profiling and validation?▼

Yes, you need Python to run SGLang server profiling, specifically utilizing Python's sglang.test modules. These modules are required to validate model accuracy and execute the system process controls needed for generating performance traces.

Can I validate model accuracy before generating a performance trace?▼

Yes, you can validate model accuracy before generating a performance trace by running quick evaluation scripts. This ensures your SGLang server setup is correct and the model is functioning properly before you begin performance analysis.

What is the best way to identify bottlenecks in a large language model server?▼

The best way to identify bottlenecks in a large language model server is to generate detailed profiling traces of your SGLang instance. By analyzing these execution workflows in Perfetto or Chrome, you can pinpoint latency issues and optimize deployment.

Related Skills

Explore More Software Engineering Skills→
gaebalaigaebalai
2

raspi-can-bootstrap

Automate CAN bootstrapping for Raspberry Pi vehicle diagnostics environments.

Community
Advanced
inehemiasminehemiasm

perfetto-trace-analysis

Analyze Android Perfetto traces to identify root causes of latency, memory, and jank issues.

Community
Advanced
SethyPagnaSethyPagna

tensorrt-llm

Optimize large language model inference on NVIDIA GPUs with TensorRT-LLM.

Community
Advanced
FutureMLS-LabFutureMLS-Lab
556

generate-profile

Generate end-to-end performance profiling traces for SGLang server runs.

Official
Intermediate