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/.agents/skills/large-class-style/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/large-class-style)<a href="https://agentmods.dev/skills/sgl-project/sglang/large-class-style"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/large-class-style/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/large-class-style"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/large-class-style.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 | $0.00051 | $0.01800 |
| Opus 5.5 | $0.00020 | $0.00720 |
| Sonnet 5.5 | $0.00010 | $0.00360 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
large-class-style 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 2mo 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Style for Scheduler / TokenizerManager / ModelRunner
Conventions for SGLang's three large classes:
Scheduler—python/sglang/srt/managers/scheduler.pyTokenizerManager—python/sglang/srt/managers/tokenizer_manager.pyModelRunner—python/sglang/srt/model_executor/model_runner.py
1. Frozen Code
- Some core files are frozen: orchestration-only — a thin composition root that constructs collaborators, wires them, delegates to them, and coordinates the calls. They must stay that way.
- Domain logic does not belong in a frozen file; it lives in a collaborator class in its own module.
1.1 Why
- The file is a thin orchestrator over collaborator classes; freezing keeps it that way and stops it growing back into a god class.
- Keeping domain logic in collaborators (their own files) is what makes per-file code ownership, single responsibility, and unit testing possible.
- The orchestrator is the composition root: it may know about every collaborator, because wiring and sequencing them is its job. Coordination stays here — domain logic does not.
1.2 Frozen files
python/sglang/srt/model_executor/model_runner.py
1.3 Allowed: orchestration
Every statement refers to a collaborator and is one of:
- Construct — a short
init_<thing>helper whose body is essentially a single construction (follows §2); usemaybe_init_<thing>with a one-line gate when conditional. - Wire — a short call that runs the helper from the orchestrator (e.g. in
__init__). - Delegate — calls to a collaborator's methods at the necessary call sites (
self.foo.run(...)). - Coordinate — the minimal control flow that selects or orders the above: an
ifchoosing whether / which collaborator to wire or call, the order of calls, threading one call's result into the next.
- Heuristic: a statement is allowed only if it constructs, wires, delegates, or selects/orders those — never if it computes or transforms a value beyond passing arguments and results through.
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.
- 2mo ago First seen · 140 lines · 51 tokens per session scan A e4c7ffb53e47
large-class-style is a skill published in the GitHub repository sgl-project/sglang (38,324 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,800 once invoked, about $0.0002 per session on Opus 5.5. 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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