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generate-profile

Profile SGLang server runs and capture Chrome-compatible traces for performance analysis.

1|Updated May 8, 2026
Search Tags:#profiling#trace#server#benchmark#perf#sglang
One-click install
npx skills add https://github.com/dyyoungg/sglang-dev --skill generate-profile-dyyoungg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: generate-profile
Source: https://github.com/dyyoungg/sglang-dev/tree/main/.claude/skills/generate-profile
Command: npx skills add https://github.com/dyyoungg/sglang-dev --skill generate-profile-dyyoungg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides an end-to-end profiling workflow for SGLang server runs, enabling developers to identify perf bottlenecks and latency sources by generating Chrome-compatible traces.

Core Features & Use Cases

  • End-to-end profiling: Launch a server, validate readiness, and capture a Chrome-compatible trace for performance analysis.
  • Guided workflow: Step-by-step instructions to reproduce profiling in local or GPU-enabled environments.
  • Use Case: Use profiling to optimize startup time, throughput, and memory usage in model-serving deployments.

Quick Start

Run the profiling workflow against a live SGLang server to produce a trace.

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 identify performance bottlenecks?▼

SGLang server profiling captures Chrome-compatible traces end-to-end to identify performance bottlenecks and latency sources. It orchestrates server launch, health validation, trace collection, and reporting through a reproducible workflow for both local and GPU-enabled deployments.

What is the best way to capture a trace for SGLang performance analysis?▼

Capturing a trace for SGLang performance analysis involves running an end-to-end profiling workflow that launches the server, validates readiness, and collects Chrome-compatible traces. This guided process ensures reproducible profiling results for optimizing startup time, throughput, and memory usage.

Can I use this profiling workflow in a local development environment?▼

Yes, the SGLang profiling workflow applies to local development and GPU-enabled deployments. It provides step-by-step instructions to reproduce server profiling and capture Chrome-compatible traces regardless of your environment setup.

Why do I need a Chrome-compatible trace for my model serving deployment?▼

A Chrome-compatible trace is needed to visualize and analyze performance bottlenecks in your model-serving deployment. It pinpoints latency sources affecting SGLang server startup time, throughput, and memory usage.

Does SGLang profiling optimize startup time and memory usage automatically?▼

SGLang profiling does not optimize automatically; it identifies performance bottlenecks by generating a trace. You analyze the trace results to manually optimize SGLang server startup time, throughput, and memory usage.

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