Agent skill

Cookbook Add Model

by sgl-project in sgl-project/sglang

Add a new model to the SGLang Cookbook (docs/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an…

Apache-2.0Auto-check passedFrontend & Design

Install Cookbook Add Model

skills CLI
$ npx skills add sgl-project/sglang --skill cookbook-add-model -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sgl-project/sglang cookbook-add-model --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sgl-project/sglang.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/cookbook-add-model .claude/skills/cookbook-add-model && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cookbook-add-model
GitHub stars
37k
Used in
2 other repos
Token cost
~4k tokens
SKILL.md length
1,607 words
Files
11 (incl. references)
Skills in repo
32
Repo updated
First seen
Licence
Apache-2.0

At a glance

Add a new model to the SGLang Cookbook (docs/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an…

  • Works in 6 steps: Collect inputs → Instantiate the template → Validate → …
  • Tasks that involve React components
  • SKILL.md covers Architecture at a glance, Phase 1 — Collect inputs, Phase 2 — Instantiate the… and Phase 3 — Validate, plus 4 more sections
  • Calls git, gh and python

What it does

Cookbook Add Model is an agent skill from sgl-project/sglang. Add a new model to the SGLang Cookbook (docs/, 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.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `references/authoring-reference.md`, `references/diffusion-authoring.md` and `references/engine-axis.md`).

It sits in Frontend & Design, covering React components and Markdown. It works with SGLang. The repository describes itself as: SGLang is a high-performance serving framework for large language models and multimodal models. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve React components
  • Tasks that involve Markdown

Example prompts

  • “/cookbook-add-model”

Requirements

  • Python 3
  • Docker

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Collect inputs
  2. Instantiate the template
  3. Validate
  4. Interactive testing
  5. Prose & config tips
  6. Review

What it can do on your machine

Read from SKILL.md and the folder at commit f620d73. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • gh
    • python
    • docker
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, gh and docker, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cookbook Add Model loads about 4k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,607 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sgl-project/sglang at commit f620d73, republished under its Apache-2.0 licence (© sgl-project). 1,607 words, ~4,023 tokens.

Download SKILL.mdSave it as .claude/skills/cookbook-add-model/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
cookbook-add-model
description
Add a new model to the SGLang Cookbook (docs/, 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.
disable-model-invocation
true

Add a model to the SGLang Cookbook

Migrating an existing legacy-template page (one that imports a monolithic …/autoregressive/<slug>-deployment.jsx generator)? Use the cookbook-migrate-model skill instead — same target format, but the legacy page (not the user) is the source of truth.

The cookbook is config-driven: two shared engines contain NO model-specific code — docs/src/snippets/_deployment.jsx (the 5-dim deploy matrix) and _playground.jsx (the diff-based override Playground). Adding a model = adding data: a per-model config (+ optional benchmarks) consumed by both engines, plus an MDX page that imports them. No engine edits.

Instantiate the model-agnostic template (NOT a clone of any live cookbook — the template is decoupled and covers all hardware + all axes):

  • templates/config.jsx.tmpl → docs/src/snippets/configs/<hf-org>/<model-slug>.jsx
  • templates/benchmarks.jsx.tmpl → …/<model-slug>-benchmarks.jsx (skip if no numbers)
  • templates/page.mdx.tmpl → docs/cookbook/<category>/<Vendor>/<ModelName>.mdx

The template uses explicit __TOKEN__ placeholders; you fill them, prune what the model lacks, and replace the EXAMPLE cells with verified recipes. DeepSeek-V4 is a populated instance you can consult, but is not the template.

Deep references (read on demand, don't inline):

Architecture at a glance

┌─────────────────────────────────────────────────────────────────┐
│  cookbook/<category>/<Vendor>/<Model>.mdx                       │
│  import { Deployment } from "/src/snippets/_deployment.jsx";    │
│  import { Playground } from "/src/snippets/_playground.jsx";    │
│  import { config }     from "/src/snippets/configs/.../X.jsx";  │
│  <Deployment config={config} />   <Playground config={config} />│
└─────────────────────────────────────────────────────────────────┘
              │ (config passed as React prop)
              ▼
┌─────────────────────────────────────────────────────────────────┐
│  src/snippets/configs/<vendor>/<model>.jsx                      │
│  export const config = {                                        │
│    supportedHardware, variants, quantizations, strategies, ...  │
│    cells: [ { match:{hw,variant,quant,strategy,nodes},          │
│              env:[...], flags:[...] }, ... ],   // 5-dim matrix  │
│    playgroundFeatures: { attention, moe, parsers, ... },        │
│  };                                                             │
└─────────────────────────────────────────────────────────────────┘
              │ (consumed by BOTH engines — no model code in engines)
              ▼
┌──────────────────────────────────┬──────────────────────────────┐
│  _deployment.jsx                 │  _playground.jsx             │
│  Renders the verified matrix;    │  Renders override chips +    │
│  one cell → its env/flags.       │  diff against the cell.      │
└──────────────────────────────────┴──────────────────────────────┘

The two widgets stay in sync via: the URL hash (deploy mirrors its selection; playground reads it), the sglang-deploy-sel custom event (deploy dispatches on every change; playground listens — replaceState doesn't fire hashchange), and the shared sglang-deploy-env localStorage key (HOST/PORT placeholders).

The main template is autoregressive. Diffusion pages use templates/diffusion-page.mdx.tmpl plus references/diffusion-authoring.md; do not force the autoregressive deployment matrix on them. Omni pages follow their own category structure. All categories still obey the Mintlify / NEW-tag / docs.json / category-card / validation rules below.


Interactive, multi-step workflow. Collect inputs incrementally — don't ask for everything upfront. The real work is the verified cells[] recipes + measured benchmarks (Phases 2 + 4); everything else is filling the template.

Phase 1 — Collect inputs

  1. Model card — HuggingFace repo/URL. Fetch the page and extract description, param count, architecture, context length, license. (Fetching guards factual bugs like an off-by-a-few-B param count.) If the model isn't public, ask the user.
  2. Variants / quantizations — keep separate: variants are size/mode (e.g. Flash/Pro, Instruct/Thinking); quantizations come from the HF card / linked repos (BF16/FP8/FP4/…). Default to BF16 when a full-precision repo exists.
  3. Tested hardware + parallelism — which platforms are actually tested, and TP/EP/DP for each. List only tested hw (unlisted greys out).
  4. Verified launch recipes — the full sglang serve flags per (hw × variant × quant × strategy × nodes) combo → these become cells[]. Rewrite any python -m sglang.launch_server to sglang serve form.
  5. sglang version / image tag — ask which sglang build the recipes + benchmarks ran on (a release like 0.5.x, or main/nightly). Never guess or hallucinate it. This one tag fills dockerImages and the benchmarks' sglang_version; when the user is unsure, default the image to lmsysorg/sglang:dev (nightly) rather than inventing a release.
  6. Pre-flight: gh pr list --repo sgl-project/sglang --search "<model>" (dup check).

Hardware reference (the shared HARDWARE_CATALOG in _deployment.jsx). A GPU not in this table (RTX PRO 6000, GH200, future chips) goes in the model's own config.hardware ({id,label,vram,vendor}) — the engine merges it in; don't edit the engine catalog:

PlatformVendorVRAMDocker image
H100NVIDIA80GBlmsysorg/sglang:<ver>
H200NVIDIA141GBlmsysorg/sglang:<ver>
B200NVIDIA192GBlmsysorg/sglang:<ver>
B300NVIDIA288GBlmsysorg/sglang:<ver> (or -cu130 when required)
GB200NVIDIA192GBlmsysorg/sglang:<ver> (or -cu130)
GB300NVIDIA288GBlmsysorg/sglang:<ver> (or -cu130)
DGX SparkNVIDIA128GB (unified)lmsysorg/sglang:<ver> — needs a CUDA 13 build
MI300XAMD192GBlmsysorg/sglang:<ver>-rocm720-mi30x
MI325XAMD256GBlmsysorg/sglang:<ver>-rocm720-mi30x
MI350XAMD288GBlmsysorg/sglang:<ver>-rocm720-mi35x
MI355XAMD288GBlmsysorg/sglang:<ver>-rocm720-mi35x
  • Image tag (<ver>): don't guess — ask the user for the tag the recipes ran on, or default to dev (nightly). The same tag goes in dockerImages and benchmarks' sglang_version; the engine falls back to lmsysorg/sglang:dev for any unmapped hw.
  • TP sizing (sanity-check recipes): weight_GB / gpu_mem, round up to a power of 2, ~20–30% headroom. BF16 ≈ params×2 GB, FP8 ≈ ×1, FP4 ≈ ×0.5. MoE → total weight, not active params. FP4 is Blackwell-only (B200/B300/GB200/GB300/DGX Spark). GB200/GB300 single-node hosts are typically 4 GPUs (TP=4 ceiling); a DGX Spark is 1 GPU, so its only multi-GPU topology is TP=2 across 2 nodes.
  • Platform flags: Blackwell may need --attention-backend trtllm_mha; AMD typically needs --attention-backend triton + env SGLANG_USE_AITER=1 / SGLANG_ROCM_FUSED_DECODE_MLA=0 (check AITER TP constraints, e.g. heads_per_gpu % 16 == 0).
  • EP (MoE): 8-GPU NVIDIA --tp 8 --ep 8; AMD EP = TP; small NVIDIA (TP≤4) omit --ep unless benchmarked. (The template's AMD example cell shows these.)
Show full SKILL.md (852 more words)Show less

Phase 2 — Instantiate the template

For a diffusion model, instantiate templates/diffusion-page.mdx.tmpl and keep the shared DiffusionModelTags component, plus templates/diffusion-config.jsx.tmpl for the opt-in scoped command builder. Put the compact install command and builder in §1 Quick start. The first two paragraphs in §2 Model capabilities are not generic filler: they must state the model's capability range, strongest differentiator, when to choose it, and at least one real deployment or capability boundary. Put orthogonal runtime features in scope: "serve" or scope: "request", not in the base recipe; use the schema from references/diffusion-authoring.md.

  1. Copy the three template files to their target paths (above). Note the two vendor-folder conventions: under configs/ the folder is the HuggingFace org (deepseek-ai); under cookbook/ it's the display vendor (DeepSeek).

  2. Replace every __TOKEN__: __MODEL_DISPLAY__, __MODEL_SLUG__, __HF_ORG__, __HF_REPO__, __REASONING_PARSER__, __TOOLCALL_PARSER__, __ONE_LINER__. Verify none remain: grep -rn '__[A-Z_]*__' <new files>.

  3. Prune to what the model supports (delete, don't stub) — using references/authoring-reference.md:

    • supportedHardware + the EXAMPLE cells: keep your tested families; delete the mi* ids + AMD example cell if no AMD recipe, etc. A GPU not in the shared catalog (e.g. RTX PRO 6000) → declare it in config.hardware and add its id here.
    • playgroundFeatures axes: remove the megamoe backend option + the megamoeQuant block from the moe axis (non-Blackwell-MoE), delete hisparse (non-DSA), pdDisagg/router (no PD), the parsers axis (no parsers), etc.
    • quantizations / variants: drop what the model doesn't ship; collapse variants to single default if there's no variant axis (then drop the variant half of modelNames/defaultAccuracy keys).
  4. Fill cells[] with the verified recipes from Phase 1 (replace every EXAMPLE cell; set verified: true only on tested combos), and modelNames with real HF slugs, dockerImages for your hw (use the Phase-1 tag, or default lmsysorg/sglang:dev — never a guessed release; key by hw, or a more specific key — hw|variant|quant, variant|quant, hw|quant|strategy, hw|quant, resolved in that order before hw — when one variant/quant/strategy needs its own image), multiNodeHints only for fabric-specific hw (e.g. gb200).

  5. Diffusion pages: add the ComfyUI section. Every diffusion cookbook page ends with a ## <n>. Run in ComfyUI section so a reader never has to guess whether the model is reachable from ComfyUI. It is one component; the per-model facts live in the component, not the page:

    mdx
    ## <n>. Run in ComfyUI
    
    import { ComfyUISupport } from '/src/snippets/diffusion/comfyui-support.jsx';
    
    <ComfyUISupport model="<key>" />

    Pick model from the table in docs/src/snippets/diffusion/comfyui-support.jsx. Use the model's own key when it has an entry (its executor or dedicated node differs); otherwise use the generic image or video. A model gets its own key only when the plugin actually treats it specially — an entry in executor_class_dict (python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/core/generator.py) or a dedicated node. Adding a key without the matching plugin support makes the page lie.

Site-wiring (do all three)
  • docs/docs.json — add the page under Cookbook → <category> → <Vendor>, at the top of that vendor's pages (root-relative, no .mdx: cookbook/<category>/<Vendor>/<Model>). New vendor group → insert in the section's local ordering.
  • NEW-tag hygiene — the new page keeps tag: NEW (from the template). Scan the vendor dir for existing NEW and strip it from siblings; verify ≤1: grep -rn 'tag: NEW' docs/cookbook/<category>/<Vendor>/ → at most one result. (Scan files; don't assume the first docs.json entry holds NEW.)
  • Homepage card — docs/cookbook/<category>/intro.mdx: if the org already has a <Card>, update only its href (keep img). If the org is new, add a <Card> (title = nav-group name; keep card order aligned with docs.json) and create its logo: ask the user for the brand logo, then generate the conforming icon-only 940×525 RGBA transparent PNG → docs/cards/logos/<org-slug>.png per references/vendor-logo.md (track with git add -f — *.png is gitignored repo-wide). Never invent or copy a logo.

Phase 3 — Validate

bash
cd docs
mint validate        # frontmatter, missing nav entries, MDX/JSX errors
mint broken-links
mint dev             # visual smoke test at http://localhost:3000/cookbook/<category>/<Vendor>/<Model>

Spot-check: cells render sensible commands; URL-hash nav persists across reload; the Playground inherits the Deploy selection live; each axis toggle produces the expected diff; Docker mode wraps in docker run with the right image; multi-node cells emit the hints + --nnodes N; cURL resolves the model name; the NEW badge shows on the new page and is gone from same-vendor siblings; the homepage card points to the new model.

Phase 4 — Interactive testing

The user deploys each cell, runs the benches, and pastes results; you fill the data:

  • mark each tested cells[] entry verified: true (absent = yellow/unverified badge);
  • fill the <model>-benchmarks.jsx entries (one per cell match) with measured speed/accuracy + the sglang_version the user reports (don't invent one — the template's 0.0.0 is a deliberate TODO); set model-level defaultAccuracy per variant. Leave a cell's entry as a bare match stub if it has no numbers yet (the card shows "pending").

Phase 5 — Prose & config tips

Read references/mintlify-authoring.md first (it carries the parser-output-shape / thinking-mode / Output-Example / no-hardcoded-sampling rules + the Mintlify forbidden-syntax list). Then rewrite the MDX prose from the HF card + user notes: §1 Model Introduction (description, links, params, license, variants table), §2 Configuration Tips (hw-specific tuning, caveats), §3 Advanced Usage (Reasoning / Tool-Calling / HiCache — keep only what applies; match the reasoning example to the parser's output shape; each runnable block gets an **Output Example:**).

For diffusion pages, follow the category-specific Quick start and capability contract in references/diffusion-authoring.md. Run node docs/scripts/check_cookbook_configs.mjs to verify the tag widget and introduction structure before rendering the page.

Phase 6 — Review

/cookbook-review-pr <PR number>

Git workflow

Always branch — never commit to main directly.

bash
git checkout -b add-<model>-cookbook
git add docs/src/snippets/configs/<hf-org>/<slug>.jsx \
        docs/src/snippets/configs/<hf-org>/<slug>-benchmarks.jsx \
        docs/cookbook/<category>/<Vendor>/<Model>.mdx \
        docs/docs.json docs/cookbook/<category>/intro.mdx
git commit -m "Add <Display-Name> cookbook"
git push -u origin add-<model>-cookbook
gh pr create --title "Add <Display-Name> cookbook" --body "..."

© sgl-project, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in .agents/skills/cookbook-add-model of sgl-project/sglang.

  • SKILL.md
  • references/authoring-reference.md
  • references/diffusion-authoring.md
  • references/engine-axis.md
  • references/mintlify-authoring.md
  • references/vendor-logo.md
  • templates/benchmarks.jsx.tmpl
  • templates/config.jsx.tmpl
  • templates/diffusion-config.jsx.tmpl
  • templates/diffusion-page.mdx.tmpl
  • templates/page.mdx.tmpl

Open the folder on GitHubat commit f620d73

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sgl-project/sglang, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Cookbook Add Model

What does Cookbook Add Model do?

Add a new model to the SGLang Cookbook (docs/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an…. Cookbook Add Model is an agent skill from sgl-project/sglang.json nav entry, NEW-tag hygiene, and the homepage vendor card.

When should I use Cookbook Add Model?

Cookbook Add Model fits situations like: tasks that involve React components; tasks that involve Markdown.

How do I install Cookbook Add Model in Claude Code?

Run `npx skills add sgl-project/sglang --skill cookbook-add-model -a claude-code`. Or copy the skill folder (.agents/skills/cookbook-add-model in sgl-project/sglang) into .claude/skills/cookbook-add-model in your project. Claude Code loads it when a task matches its description.

How do I install Cookbook Add Model in Codex?

Run `npx skills add sgl-project/sglang --skill cookbook-add-model -a codex`. Or copy the skill folder (.agents/skills/cookbook-add-model in sgl-project/sglang) into .agents/skills/cookbook-add-model in your project. Codex loads it when a task matches its description.

Can I use Cookbook Add Model in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sgl-project/sglang --skill cookbook-add-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cookbook-add-model, .gemini/skills/cookbook-add-model, .github/skills/cookbook-add-model and .opencode/skills/cookbook-add-model in your project.

What does Cookbook Add Model need to run?

Going by SKILL.md and its folder, Cookbook Add Model needs the command-line tools its instructions call (git, gh, python, docker and node). Our summary lists: Python 3; Docker.

Does Cookbook Add Model access the network?

SKILL.md contains no URLs. Its commands use git, gh and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Cookbook Add Model safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cookbook Add Model use?

Cookbook Add Model is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cookbook Add Model use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.

What are the alternatives to Cookbook Add Model?

Skills that share tags, products or a category with Cookbook Add Model: Fumadocs (better-notify/better-notify, 313 stars), Rasengan Pages (rasengan-dev/rasenganjs, 124 stars), Blog Chart (AgriciDaniel/claude-blog, 2.3k stars) and React Component Documentation (getsentry/sentry, 46k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cookbook Add Model?

sgl-project (a GitHub organization) maintains it in sgl-project/sglang, which has 36,907 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 9, 2026.

Source: sgl-project/sglang on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.