sglang-prod-incident-triage
SGLang Serving Debug
Overview
Use this skill to turn a live serving problem into a debug path you can replay.
Use one loop:
- collect a baseline bundle
- save the failing request or crash dump
- replay on a clean target
- only then switch tools
Do not start with profiling.
This skill should work with more focused skills instead of re-implementing them:
debug-cuda-crashwhen replay plus coredump points to a CUDA crash pathdebug-distributed-hangwhen the problem is clearly a TP/PP/DP/EP hang
More from bbuf/sglang-auto-driven-skills
h100
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/sgl-workspace/sglang`, and use the ready H100 remote environment for SGLang development and validation. Use when a task needs remote CUDA work, GPU-backed smoke tests, diffusion checks, or a safe remote copy instead of local-only execution.
34h100-sglang-diffusion
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
33llm-serving-auto-benchmark
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.
19llm-torch-profiler-analysis
Unified LLM torch-profiler triage skill for `sglang`, `vllm`, and `TensorRT-LLM`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
18sglang-sota-performance
End-to-end SGLang SOTA performance workflow. Use when a user names an LLM model and wants SGLang to match or beat the best observed vLLM and TensorRT-LLM serving performance by searching each framework's best deployment command, benchmarking them fairly, profiling SGLang if it is slower, identifying kernel/overlap/fusion bottlenecks, patching SGLang code, and revalidating with real model runs.
15sglang-minimax-m2-series-optimization
PR-backed and current-main optimization manual for the `MiniMaxAI/MiniMax-M2` series, including M2, M2.1, M2.5, M2.7, and M2.7-highspeed. Use when Codex needs to recover, extend, or audit MiniMax-specific optimizations, TP QK norm/all-reduce behavior, parser contracts, distributed runtime behavior, quantized loading, or backend-specific validation.
15