Install agent skills

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
• triton-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.

When are Triton kernels useful? Two main cases:

• Portability across NVIDIA and AMD — a single Triton kernel runs on both CUDA and ROCm without modification. No vendor-specific code needed.
• Fusing multiple ops to reduce memory traffic — operations like softmax (5 PyTorch ops, ~8MN memory ops) become a single kernel (2MN ops) with a ~4x reduction in DRAM round-trips. Any chain of element-wise + reduction ops that PyTorch executes as separate kernels is a fusion opportunity.

Example Triton kernels built with this skill:

• jaygala223/triton-layernorm — fused LayerNorm with fp32 accumulation, ~1.45x faster than PyTorch on V100.

Examples:

# 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