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/clean-startup-log/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/clean-startup-log)<a href="https://agentmods.dev/skills/sgl-project/sglang/clean-startup-log"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/clean-startup-log.svg" alt="Measured on agentmods" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 282 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00045 | $0.05058 |
| Opus 5 | $0.00023 | $0.02529 |
| Sonnet 5 | $0.00009 | $0.01012 |
| Haiku 4.5 | $0.00005 | $0.00506 |
Grade A, and why
clean-startup-log scanned grade A with 0 findings 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 8d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Up SGLang Server Startup Logs
Goal: ensure the server startup log is clean and minimal, with no spurious warnings, deprecation messages, or unformatted prints from third-party libraries.
Workflow
1. Launch a server and capture the log
uv run sglang serve --model-path Qwen/Qwen3-8B 2>&1 | tee /tmp/startup_log.txt
Wait until the server prints The server is fired up and ready to roll!, then Ctrl-C.
For TP>1 testing:
uv run sglang serve --model-path Qwen/Qwen3-8B --tp 2 2>&1 | tee /tmp/startup_log.txt
For MoE / hybrid-SWA models (e.g. gpt-oss), test separately — they exercise different code paths:
uv run sglang serve --model-path openai/gpt-oss-20b 2>&1 | tee /tmp/startup_log.txt
2. Compare against the clean reference log
Read /tmp/startup_log.txt and compare it against the reference log at the bottom of this file. Identify lines that:
- Do NOT have the
[timestamp]or[timestamp TPx]logger prefix - Contain
WARNING,deprecated,is deprecated, or similar noise - Are printed by third-party libraries (transformers, torchao, NCCL, Gloo, tqdm, etc.)
- Are duplicate/redundant with information already logged by SGLang
- Appear multiple times due to
ModelConfigbeing constructed in multiple processes
3. Classify each noisy line
For each noisy line, determine:
| Category | Action |
|---|---|
| SGLang code using wrong API | Fix the SGLang code (e.g., replace deprecated API with new one) |
| SGLang code logging at wrong level | Change log level (e.g., warning -> debug for non-actionable messages) |
| Duplicated across processes | Downgrade to debug — info logged in one process becomes noise in 3-4 |
| Third-party lib prints at import time | Suppress the logger or redirect stdout during that import |
| C-level print from .so library | Redirect fd 1 during the specific C call, or accept it if too invasive |
| Real warning the user should see | Keep it |
4. Present findings before fixing
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.
- 8d ago First seen · 283 lines · 45 tokens per session scan A bead524743d1
clean-startup-log is a skill published in the GitHub repository sgl-project/sglang (35,615 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 5,058 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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