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Lsglang
Lsglang contient 11 skills collectées depuis guqiong96, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Add a new model to the SGLang Cookbook (docs_new/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an MDX page, the docs.json nav entry, NEW-tag hygiene, and the homepage vendor card. Interactive, multi-phase. Run with /cookbook-add-model.
Migrate a legacy-template SGLang cookbook page (monolithic per-model generator under docs_new/src/snippets/autoregressive/) onto the config-driven template (shared _deployment.jsx / _playground.jsx engines + per-model config). Use when asked to migrate, convert, or port an existing cookbook page — NOT for brand-new models (use cookbook-add-model for those). Run with /cookbook-migrate-model <Model page name, e.g. GLM-5.1>.
Review a pull request against the SGLang Cookbook (docs_new/, Mintlify) contribution checklist — the config-driven format (per-model config + benchmarks JSX consumed by the shared _deployment.jsx / _playground.jsx engines). Run with /cookbook-review-pr <PR number>.
Step-by-step tutorial for adding a new lightweight JIT CUDA kernel to sglang's jit_kernel module
Unified LLM torch-profiler triage skill for `sglang`, `vllm`, `TensorRT-LLM`, and `TokenSpeed`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.
Use when choosing the fastest SGLang Diffusion flags for a model, GPU, and VRAM budget.
Use when adding a new diffusion model or Diffusers pipeline to SGLang.
Use when quantizing a diffusion DiT with NVIDIA ModelOpt and making the resulting FP8 or NVFP4 checkpoint loadable, verifiable, and benchmarkable in SGLang Diffusion.
`__init__` style for SGLang `Scheduler`, `TokenizerManager`, and `ModelRunner`. Use when modifying the `__init__` of any of these three classes, or reviewing changes that add new construction logic to them.
Verify mechanical refactoring commits by requiring a reproducible transform script (gist) in the PR description. Use when doing or reviewing file splits, function moves, or module extractions.