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mit-han-labmit-han-labOfficial·1 Agent Skills Included

KernelWiki

Blackwell and Hopper GPU kernel optimization knowledge base

Answers questions about optimizing NVIDIA Blackwell and Hopper GPU kernels, covering tcgen05, TMEM, NVFP4, FlashAttention-4, DeepGEMM, and fused MoE. Eliminates hours of digging through upstream PRs, papers, and blogs by providing pre-synthesized, cross-referenced wiki pages with verified code snippets. Lets engineers query by symptom, technique, hardware feature, or repository to find proven optimization patterns fast.
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npx skills add mit-han-lab/KernelWiki --all -g -y
Available:

Tells the agent how to navigate the three-layer knowledge base using query scripts, page IDs, and cross-reference indices to answer Blackwell and Hopper kernel questions with cited sources.

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KernelWiki

Provide expert guidance for optimizing NVIDIA GPU kernels on Blackwell and Hopper architectures.

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Frequently Asked Questions

FAQPage Schema
How to install KernelWiki?▼

Run `npx skills add mit-han-lab/KernelWiki --all -g -y` in your terminal to install the skill globally.

What is KernelWiki used for?▼

It is a structured knowledge base for optimizing NVIDIA Blackwell (SM100) and Hopper (SM90) GPU kernels, covering hardware features, techniques, and real kernel case studies like FlashAttention-4 and DeepGEMM.

How do I search the KernelWiki knowledge base?▼

Use the bundled scripts, for example `python3 scripts/query.py "warp specialization"` or `python3 scripts/get_page.py kernel-flash-attention-4`, to search by keyword, tag, architecture, or page ID.

Does KernelWiki include real code examples?▼

Yes. Every technique, kernel, and language page is validator-enforced to include at least a compilable code snippet, and performance claims cite their original source.

Can I use KernelWiki without GPU programming experience?▼

Yes. You can ask questions in plain English and your agent will query the wiki, follow cited sources, and return synthesized answers with confidence levels.

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