Sglang Model Day0 Support logo

Sglang Model Day0 Support

Community
BBuf
sglang-model-day0-support

Build or audit an evidence-driven SGLang Day-0 support program for a new LLM, VLM, MoE, hybrid-attention, or speculative-decoding model. Use when Codex needs to map a model architecture into SGLang runtime work, design a public support PR DAG, create validation and release gates, sanitize private development evidence, distinguish Day-0 requirements from later fixes or optimizations, or review whether an existing model-support PR is release-ready.

Overview

PublisherBBuf
RepositoryAI-Infra-Auto-Driven-SKILLS
Skill namesglang-model-day0-support
Stars
946
Forks
83
Bundled files
17
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BBuf on GitHub. Read the source before you install it.

Installation

Install the Sglang Model Day0 Support AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS/tree/main/skills/model-optimization/sglang-model-day0-support
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS.git /tmp/AI-Infra-Auto-Driven-SKILLS
mkdir -p .claude/skills
cp -r /tmp/AI-Infra-Auto-Driven-SKILLS/skills/model-optimization/sglang-model-day0-support .claude/skills/sglang-model-day0-support
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sglang Model Day0 Support in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sglang Model Day0 Support on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sglang Model Day0 Support is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SGLang Model Day-0 Support

Turn a model release into a reviewable SGLang support program. Produce evidence, implementation boundaries, validation gates, and public release artifacts—not only a launch command.

Use the maintained source contracts when applying historical evidence to a current branch. The review/history corpora retain their own capture dates; they do not certify today's dispatch or numerical defaults. For kernel replacements, verify that the real model executes the candidate before treating end-to-end tests as coverage.

Start Here

  1. Read day0-contract.md completely.
  2. Copy assets/day0-bundle/ into the active model-support workspace.
  3. Lock immutable model, weight, tokenizer/processor, SGLang, dependency, and image revisions before describing support.
  4. Read evidence-audit.md before citing PRs.
  5. If any input is non-public, read sanitization.md before writing public output.
  6. Read only the relevant case study:

Do not implement model code until the scope contract, architecture gap map, PR DAG, and minimum validation matrix agree.

Workflow

1. Lock the release cut

Fill scope-contract.md and release-lock.md.

  • Record full immutable revisions. Reject floating branches, mutable tags, and unpinned object-store paths as release evidence.
  • Name required hardware, precision, modality, serving topology, protocol, and performance lanes.
  • Separate Day-0 commitments from explicitly deferred work.
  • Record dependency ownership: upstream package, SGLang patch, bundled wheel, image layer, or documented external prerequisite.
  • Treat unavailable weights or hardware as an explicit blocked lane, not an implicit pass.

2. Build the architecture gap map

Compare the public model configuration/reference implementation with the locked SGLang source. Inspect each capability family in the Day-0 contract.

For each gap, record:

  • checkpoint/config field and expected tensor or protocol contract;
  • closest SGLang implementation and why reuse is safe or unsafe;
  • files/classes/functions expected to change;
  • required fallback before any specialized kernel;
  • evidence class and validation owner;
  • interaction risks with state, graph capture, parallel layout, or protocol.

Use public model-family PR history when available. Do not infer support from a shared architecture name alone.

3. Classify the work

Assign exactly one class:

  • day0-required: needed to load, serve, produce correct public API output, or satisfy an announced release lane.
  • post-day0-fix: discovered after the release cut and required to restore a previously claimed behavior.
  • performance-only: improves a supported path without defining correctness.
  • experiment-or-revert: unmerged, reverted, unreachable, or retained only as a negative result.

If one patch mixes classes, split it or explain why atomic review is impossible. Never promote an experiment merely because it exists on a development branch.

4. Design the PR DAG

Fill pr-dag.md. Prefer reviewable PRs with explicit dependencies:

  1. shared public infrastructure;
  2. model config, loader, and eager correctness spine;
  3. state/cache and required parallel composition;
  4. protocol, speculative, and multimodal surfaces;
  5. platform-specific kernels or backends;
  6. images, cookbook, tests, and release wiring;
  7. one umbrella integration PR only when the release branch requires it.

Require each node to name its merge gate, fallback, tests, and public evidence. Avoid copying an entire development branch when a smaller auditable diff is possible.

5. Build the validation matrix

Fill validation-matrix.md. Do not construct the full Cartesian product.

Always include:

  • one eager load and deterministic short generation;
  • weight mapping and quantization post-processing;
  • chat, reasoning, tool, structured-output, streaming-split, and stop-marker behavior required by the model;
  • state allocation, prefix reuse, eviction, graph padding, and dtype/layout;
  • required TP/DP/EP/CP/DCP/PP/PD/EPD roles;
  • accuracy, memory capacity, and representative performance;
  • one fallback-path test for each specialized backend.

Add risk pairs where two features rewrite the same state, tokens, graph, collective, or transfer layout. Typical examples are speculative decoding × recurrent state, DCP × cache ownership, VLM × PD/EPD, DP attention × MoE collectives, and CUDA Graphs × alternative streams.

6. Execute the seven gates

Execute the gates from the Day-0 contract in order:

  1. source;
  2. load;
  3. protocol;
  4. state;
  5. topology;
  6. quality/performance;
  7. release.

For every pass, retain the command, revision, hardware, result, and limitation. For every failure, record whether it blocks Day-0, narrows the claim, or moves to the follow-up ledger.

Do not use server startup as evidence for output correctness, state correctness, topology composition, or performance.

7. Synthesize the public PR

Fill pr-body.md from reviewed evidence.

  • Lead with the support contract and exact public status.
  • Explain architecture deltas and implementation boundaries.
  • Link public PRs and public source paths.
  • Separate required support from optional fast paths.
  • State tested hardware, topology, precision, modality, workload, and revision.
  • Preserve known limitations and open work.
  • For large rebases, include a conflict-decision ledger: upstream behavior, model-branch behavior, chosen resolution, owner, and validation.

Run the public evidence collector only for mechanical metadata. Write motivation, implementation, and limitations manually after reading the diff.

8. Track post-Day-0 work

Fill follow-up-ledger.md.

  • Keep correctness repairs, performance work, platform extensions, and experiments/reverts separate.
  • Record which original claim each fix changes.
  • Reopen a release gate when a fix invalidates its evidence.
  • Mark default flips and reverts explicitly; do not rewrite history as if the final default was always known.

9. Validate and sanitize

Run:

bash
python3 scripts/validate_day0_bundle.py /path/to/day0-bundle

When private inputs exist, create an uncommitted denylist with one forbidden literal per line:

bash
python3 scripts/validate_day0_bundle.py \
  /path/to/day0-bundle \
  --denylist /path/to/uncommitted-denylist.txt

Collect public PR metadata when needed:

bash
python3 scripts/collect_public_pr_evidence.py \
  https://github.com/sgl-project/sglang/pull/23882 \
  --output /path/to/public-pr-evidence.json

Inspect the generated JSON, then manually read the complete diff and final mainline code. The JSON is an inventory, not a PR summary.

Reference Routing

Completion Contract

Finish only when:

  • all eight bundle files validate without unresolved markers;
  • every required capability has a pass, explicit limitation, or Day-0 blocker;
  • every cited PR has a manual diff-reviewed card or a link to one;
  • open, closed-unmerged, reverted, and experimental work is labeled correctly;
  • public performance claims retain their public source and measurement scope;
  • the release lock uses immutable artifacts;
  • the sanitization report records the public-evidence and denylist results;
  • the public PR body matches the actual release cut.

For image-first spines, lock the image digest/source commit while the branch is unstable. Companion upstream skills: .agents/skills/cookbook-add-model/ and .agents/skills/mechanical-refactor-verify/.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sglang Model Day0 Support AI skill do?

Build or audit an evidence-driven SGLang Day-0 support program for a new LLM, VLM, MoE, hybrid-attention, or speculative-decoding model. Use when Codex needs to map a model architecture into SGLang runtime work, design a public support PR DAG, create validation and release gates, sanitize private development evidence, distinguish Day-0 requirements from later fixes or optimizations, or review whether an existing model-support PR is release-ready.

Why use Sglang Model Day0 Support on TypingMind?

Because you install it once and use it with any model. Sglang Model Day0 Support is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sglang Model Day0 Support in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BBuf/AI-Infra-Auto-Driven-SKILLS/tree/main/skills/model-optimization/sglang-model-day0-support. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sglang Model Day0 Support?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sglang Model Day0 Support?

As many as you like. As long as a model supports skills, you can use Sglang Model Day0 Support with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sglang Model Day0 Support AI skill free?

It is published on GitHub by BBuf. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇