sglang-prod-incident-triage

Replay failing SGLang requests on a clean target for incident triage.

Updated Jun 29, 2026
One-click install
npx skills add https://github.com/hey-kong/sglang --skill sglang-prod-incident-triage-hey-kong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sglang-prod-incident-triage
Source: https://github.com/hey-kong/sglang/tree/main/.claude/skills/sglang-prod-incident-triage
Command: npx skills add https://github.com/hey-kong/sglang --skill sglang-prod-incident-triage-hey-kong

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Replay-first debugging flow enables rapid incident resolution for SGLang serving problems. It guides operators from baseline collection to failing-request capture, replay on a clean target, and then targeted tool use for CUDA crashes, distributed hangs, or profiling scenarios.

Core Features & Use Cases

  • Replay-first workflow turns live serving problems into reproducible debug steps.
  • It links to specialized tools for CUDA crash, distributed hang, and torch profiling when narrowed.
  • Use cases include health degradation, latency regressions, queue growth, or post-deploy behavioral changes.

Quick Start

Collect a live bundle, save the failing request or crash dump, then replay on a clean target to identify the root cause.

Frequently Asked Questions about sglang-prod-incident-triage

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

FAQPage Schema
How do I debug SGLang serving incidents like latency regressions or queue growth?▼

Debug SGLang serving incidents using a replay-first workflow that collects baseline data and captures failing requests to replay on a clean target. This approach isolates the root cause of health degradation or post-deploy behavioral changes.

What is the best way to reproduce a CUDA crash or distributed hang from an SGLang server?▼

Reproduce a CUDA crash or distributed hang by saving the failing request or crash dump from the live server. Replay this bundle on a clean target to trigger specialized tools for deterministic debugging.

How do I triage an SGLang incident step by step?▼

Triage an SGLang incident by collecting a live bundle, saving the failing request, replaying it on a clean target, and then applying targeted tools for CUDA crashes or profiling to identify the root cause.

Do I need specific dependencies to run the SGLang incident triage workflow?▼

You need the standard Python requests library to run the SGLang incident triage workflow. The process relies on standard Python tooling and integrates optional scripts and references for end-to-end support.

Can I use this replay debugging flow for post-deploy behavioral changes in SGLang?▼

Yes, you can use this replay debugging flow for post-deploy behavioral changes in SGLang. It captures the failing request from the live environment and replays it to resolve serving problems rapidly.

Why does replaying a failing request on a clean target help troubleshoot SGLang serving problems?▼

Replaying a failing request on a clean target helps troubleshoot SGLang serving problems by isolating the issue from live environment noise. This deterministic step pinpoints whether CUDA crashes or distributed hangs are caused by specific inputs.