Kernels

kernel-builder skills add

Use kernel-builder skills add to install the skills for AI coding assistants like Claude, Codex, and OpenCode. Supported skills include:

• cuda-kernels (default)
• rocm-kernels
• xpu-kernels
• cpu-kernels

Skill files are downloaded from the huggingface/kernels directory in this repository.

Skills instruct agents how to deal with hardware-specific optimizations, integrate with libraries like diffusers and transformers, and benchmark kernel performance in consistent ways.

When are CPU kernels actually helpful? Two main cases:

• Better performance on Intel Xeon — custom AVX2/AVX512 kernels (and AMX via brgemm for quantized GEMM) outperform generic PyTorch ops for element-wise and quantized workloads, especially in CPU-only or latency-sensitive serving.
• Enabling functionality that otherwise can’t run — some kernels are a hard requirement, e.g. megablocks MoE on CPU, where without the kernel you simply cannot run MXFP4.

Example CPU kernels built with this skill (available on the Hub under kernels-community):

• kernels-community/megablocks — MoE kernels with a CPU backend that enable running MXFP4 MoE models on CPU.
• kernels-community/quantization-gptq — INT4 quantized GEMM using AVX512.
• kernels-community/rmsnorm — RMSNorm with AVX2/AVX512 element-wise paths.

Examples:

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# install for Claude in the current project
kernel-builder skills add --claude

# install ROCm kernels skill for Codex
kernel-builder skills add --skill rocm-kernels --codex

# install globally for Codex
kernel-builder skills add --codex --global

# install for multiple assistants
kernel-builder skills add --claude --codex --opencode

# install to a custom destination and overwrite if already present
kernel-builder skills add --dest ~/my-skills --force