tl;dr: We built an agent skill that teaches coding agents how to write production CUDA kernels. Then we pointed Claude and Codex at two real targets: a diffusers pipeline and a transformers model. The agents produced working kernels for both, with correct PyTorch bindings and benchmarks, end to end.

Writing CUDA kernels is hard. Writing CUDA kernels that correctly integrate with transformers and diffusers is harder. There are architecture-specific memory access patterns, vectorization strategies, warp shuffle reductions, and a dozen integration pitfalls that trip up even experienced developers. It is exactly the kind of specialized, high-stakes problem where agent skills shine.

We gave coding agents the domain knowledge they need, like which GPU architecture to target, how to structure a kernel-builder project, when to use shared memory versus registers, and how to write PyTorch bindings. The agents did the rest. If you have used the LLM training skill or read We Got Claude to Teach Open Models, the pattern will feel familiar: package domain expertise into a skill, point the agent at a problem, and let it work.

The Kernel Hub solved the distribution of custom hardware kernels. You can load pre-compiled kernels from the Hub with a single get_kernel call. No builds, no flags. However, someone still needs to write the kernels. That is the gap this skill fills.

This is domain knowledge that gets lost in documentation tabs and Stack Overflow answers. An agent skill packages it into context that loads on demand.

First, let's show how to use the skill right away, then we'll dive into the details of how we benchmarked the kernels.

Source link