cutlass-skill

What problem does it solve?

Write, debug, and optimize CUTLASS and CuTeDSL GPU kernels using local source code, examples, and header references. Use when the user mentions CUTLASS, CuTe, CuTeDSL, cute::Layout, cute::Tensor, TiledMMA, TiledCopy, CollectiveMainloop, CollectiveEpilogue, GEMM kernel, grouped GEMM, sparse GEMM, flash attention CUTLASS, blackwell GEMM, hopper GEMM, FP8 GEMM, FP4 GEMM, blockwise scaling, MoE GEMM, StreamK, warp specialization CUTLASS, TMA CUTLASS, epilogue fusion, EVT (Epilogue Visitor Tree), pycute, Layout algebra, Swizzle pattern, GemmUniversal, KernelSchedule, EpilogueSchedule, CUTLASS collective builder, CUTLASS pipeline, or asks about writing high-performance CUDA kernels with CUTLASS/CuTe templates. Also use when the user wants to understand CUTLASS source code structure, compile CUTLASS examples, or debug CUTLASS template errors. This Skill focuses on providing practical guidance, repository navigation, build and debug workflows for CUTLASS and CuTeDSL projects.

Core Features & Use Cases

• Local source code exploration and quick-start templates for CUTLASS and CuTeDSL
• Build, run, and debug CUTLASS examples and CuTeDSL kernels
• Understand repository structure, headers, and common templates to diagnose issues

Quick Start

Set CUTLASS_REPO to your local path, update or clone the CUTLASS sources, then build and debug a CuTeDSL kernel.