SGLang is a framework for running inference for large language models and multimodal models, meaning it processes inputs to produce model outputs such as text or other media. It is used to serve and accelerate open AI models and related workloads.
Borrowing it
Nothing to install: this file belongs to sgl-project/sglang. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sgl-project/sglang/main/.claude/skills/sglang-prod-incident-triage/SKILL.mdgit clone --depth 1 https://github.com/sgl-project/sglangWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sgl-project/sglang/sglang-prod-incident-triage)<a href="https://agentmods.dev/skills/sgl-project/sglang/sglang-prod-incident-triage"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/sglang-prod-incident-triage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sgl-project/sglang/sglang-prod-incident-triage"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/sglang-prod-incident-triage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.02090 |
| Opus 5 | $0.00042 | $0.01045 |
| Sonnet 5 | $0.00017 | $0.00418 |
| Haiku 4.5 | $0.00008 | $0.00209 |
Grade A, and why
sglang-prod-incident-triage scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl "http://127.0.0.1:30000/set_trace_level?level=1" How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 hangllm-torch-profiler-analysiswhen the issue is already narrowed to a compute-side path
Three examples are included:
- TTFT spike with low queue time
- replay-first CUDA crash flow
- request-shaped distributed hang flow
Output Contract
Return:
- problem class
- what was checked
- strongest signal so far
- current best guess
- what was ruled out
- next step
- production risk
When To Use It
/healthor/health_generateis unhealthy- latency or throughput regressed under serving load
- queue size grows while health still looks green
- one request class times out or hangs
- the server crashes only after some requests
- outputs changed after a deploy, topology change, or weight switch
- one older commit is known-good and a newer commit is known-bad
Workflow
1. Collect a baseline bundle
If a live server is reachable, collect a read-only bundle before anything more intrusive:
python3 scripts/incident_artifact_tool.py collect-bundle \
--base-url http://127.0.0.1:30000 \
--outdir /tmp/incident_bundle
python3 scripts/incident_artifact_tool.py summarize-bundle \
/tmp/incident_bundle
If the server is protected:
python3 scripts/incident_artifact_tool.py collect-bundle \
--base-url http://127.0.0.1:30000 \
--token "$SGLANG_BEARER_TOKEN" \
--outdir /tmp/incident_bundle
The bundle script collects:
/health/health_generate/model_info/server_info/v1/loads?include=all/v1/loads?include=core,queues,disagg,spec/metrics/hicache/storage-backendon a best-effort basis
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 292 lines · 83 tokens per session scan A 7f0cfc4c6829
sglang-prod-incident-triage is a skill published in the GitHub repository sgl-project/sglang (35,828 stars, last pushed today), licensed Apache-2.0. It adds 83 tokens to every session and 2,090 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
perf-analyze
Launch and operate this repo's perf-analyze workflow, which DIAGNOSES a TensorRT-LLM serving deployment without applying changes — benchmark at one concurrency or a Pareto curve of them (tok/s/user vs tok/s/gpu), analytical SOL projection on by default (via the internal-perf-sol-analysis skill), nsys + ncu per-kernel…
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
python-debugpy
Debug Python: pdb REPL + debugpy remote (DAP).
llama-factory
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support.
trulens-diagnosis
Diagnose low evaluation scores and generate actionable improvement recommendations.
debug-with-langwatch
Root-cause production errors and misbehaving agent runs with LangWatch. Finds errored traces, inspects spans, checks monitor and evaluator scores, then narrows to a root cause. Use when something is failing or misbehaving in production (errors, bad answers, latency spikes).