add-jit-kernel Skill
Step-by-step tutorial for adding a lightweight JIT CUDA/C++ kernel to python/sglang/jitkernel (including tests & benchmarks) Published by microsoft in llm-42.
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Developers using llm-42, Includes SKILL.md, Reusable instructions
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Copy skill directory
2 months ago · Apache-2.0 license
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What is add-jit-kernel Skill?
Step-by-step tutorial for adding a lightweight JIT CUDA/C++ kernel to python/sglang/jitkernel (including tests & benchmarks) Published by microsoft in llm-42. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Developers using llm-42
Declared skill metadata
- Source file: .claude/skills/add-jit-kernel/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use add-jit-kernel Skill
- Use it for developers using llm-42.
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Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/llm-42
- Skill file: .claude/skills/add-jit-kernel/SKILL.md
What it does
Step-by-step tutorial for adding a lightweight JIT CUDA/C++ kernel to python/sglang/jitkernel (including tests & benchmarks)
Skill instructions
Tutorial: Adding a New Kernel to python/sglang/jitkernel (JIT / Lightweight) This SKILL is a step-by-step guide for adding a lightweight CUDA/C++ kernel to python/sglang/jitkernel/. Typical characteristics: - Few dependencies (usually tvm-ffi + a small subset of sglkernel utility headers) - Compiled at runtime (JIT), optimized for fast iteration - Avoids pulling heavyweight third-party/template code into AOT builds Two rules of thumb (must follow) 1. Heavyweight kernels go to sgl-kernel. If it depends on CUTLASS / FlashInfer / DeepGEMM (or similarly heavy stacks), implement it in sgl-kernel/. 2. Lightweight kernels go to jitkernel. If it is small and can be compiled independently, implement it here. Stop and use sgl-kernel instead (important) Do not add a new kernel under jitkernel if any of the following applies: - It directly depends on CUTLASS / FlashInfer (or other heavyweight third-party stacks) - It requires complex link-time integration, large template instantiations, or AOT-sty
Verified compatibility and discovery
Frequently asked questions
What is add-jit-kernel?
add-jit-kernel is a open-source AI agent skill with Copy skill directory. Step-by-step tutorial for adding a lightweight JIT CUDA/C++ kernel to python/sglang/jitkernel (including tests & benchmarks)
Who is add-jit-kernel best for?
add-jit-kernel is best for reusing agent instructions, scripts, and references.
How do I install add-jit-kernel?
Install or run add-jit-kernel using Copy skill directory. Check add-jit-kernel for the latest setup command.
Is add-jit-kernel actively maintained?
add-jit-kernel may need a closer maintenance check before production use.
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Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
