kernels/pr_666/en/cli-skills.md

# 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](https://github.com/huggingface/kernels/tree/main/kernel-builder/skills).

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

> [!TIP]
> **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`](https://huggingface.co/kernels-community)):

- [`kernels-community/megablocks`](https://huggingface.co/kernels-community/megablocks) — MoE kernels with a CPU backend that enable running MXFP4 MoE models on CPU.
- [`kernels-community/quantization-gptq`](https://huggingface.co/kernels-community/quantization-gptq) — INT4 quantized GEMM using AVX512.
- [`kernels-community/rmsnorm`](https://huggingface.co/kernels-community/rmsnorm) — RMSNorm with AVX2/AVX512 element-wise paths.

> [!TIP]
> **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`](https://huggingface.co/kernels/jaygala223/triton-layernorm) — fused LayerNorm with fp32 accumulation, ~1.45x faster than PyTorch on V100.

Examples:

```bash
# 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
```