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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sgl-project/sglang --skill sglang-diffusion-add-modelgit 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-diffusion-add-model)<a href="https://agentmods.dev/skills/sgl-project/sglang/sglang-diffusion-add-model"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/sglang-diffusion-add-model/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-diffusion-add-model"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/sglang-diffusion-add-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 System Prompt Leakage · line 404 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- medium System Prompt Leakage · line 429 Skill contains patterns that could indirectly extract system prompts through rephrasing, translation, summarization, or side-channel techniques.Fix: Guard against indirect extraction by refusing to summarize, translate, or rephrase system instructions. Add explicit anti-extraction clauses.
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 | $0.00024 | $0.09284 |
| Opus 5.5 | $0.00010 | $0.03714 |
| Sonnet 5.5 | $0.00005 | $0.01857 |
| Haiku 4.5 | $0.00002 | $0.00928 |
Grade A, and why
sglang-diffusion-add-model 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 16d 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 — 780 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a Diffusion Model to SGLang
Use this skill when adding a new diffusion model or pipeline variant to sglang.multimodal_gen.
Three Pipeline Styles
Style A: Hybrid Monolithic Pipeline (Recommended)
The recommended default for most new models. Uses a three-stage structure:
BeforeDenoisingStage (model-specific) --> DenoisingStage (standard) --> DecodingStage (standard)
- BeforeDenoisingStage: A single, model-specific stage that consolidates all pre-processing logic: input validation, text encoding, image encoding, latent preparation, timestep setup. This stage is unique per model.
- DenoisingStage: Framework-standard stage for the denoising loop (DiT/UNet forward passes). Shared across models.
- DecodingStage: Framework-standard stage for VAE decoding. Shared across models.
Why recommended? Modern diffusion models have highly heterogeneous pre-processing requirements (different text encoders, different latent formats, different conditioning mechanisms). The Hybrid approach keeps pre-processing isolated per model, avoids fragile shared stages with excessive conditional logic, and lets developers port Diffusers reference code quickly.
Style B: Modular Composition Style
Uses the framework's fine-grained standard stages (TextEncodingStage, LatentPreparationStage, TimestepPreparationStage, etc.) to build the pipeline by composition.
This style is appropriate when:
- The new model's pre-processing can largely reuse existing stages — e.g., a model that uses standard CLIP/T5 text encoding + standard latent preparation with minimal customization. In this case,
add_standard_t2i_stages()oradd_standard_ti2i_stages()may be all you need. - A model-specific optimization needs to be extracted as a standalone stage — e.g., a specialized encoding or conditioning step that benefits from being a separate stage for profiling, parallelism control, or reuse across multiple pipeline variants.
See existing Modular examples: QwenImagePipeline (uses add_standard_t2i_stages), FluxPipeline, WanPipeline, SanaPipeline, StableDiffusion3Pipeline, and ZImagePipeline.
What ships with it
1 file 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.
- 16d ago Changed 34dcd853d84e
- 1mo ago Changed · +2 lines 4ad59a61ba03
- 1mo ago First seen · 778 lines · 24 tokens per session scan A 4269fb556560
sglang-diffusion-add-model is a skill published in the GitHub repository sgl-project/sglang (37,675 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 9,284 once invoked, about $0.0001 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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