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sablin39/tilelang-cuda-skills

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Agent skills for GPU kernel development — writing, debugging, profiling, and optimizing CUDA and TileLang GPU kernels.

Обзор

Agent skills for GPU kernel development — writing, debugging, profiling, and optimizing CUDA and TileLang GPU kernels.

README

GPU Development Skills

Agent skills for GPU kernel development — writing, debugging, profiling, and optimizing CUDA and TileLang GPU kernels.

The .vscode directory is intentionally tracked as a reference to configure highlighting for humans.

TileLang Skills

TileLang skills created by Claude Opus 4.6 (probably) in Claude Code with the skill-creator plugin. Based on TileLang v0.1.9 docs and examples, validated on RTX PRO 6000 Blackwell GPUs (sm_120) with CUDA 13.1 and PyTorch 2.11.

Skill Description Key Topics
writing-tilelang-kernels Write TileLang GPU kernels from scratch or by adapting patterns Kernel anatomy, templates (GEMM, elementwise, reduction), memory scopes, T.copy/T.gemm, dynamic shapes
debugging-tilelang-programs Diagnose and fix errors in TileLang programs Failure taxonomy, T.print, AutoDD, compute-sanitizer, numerical drift, race detection
profiling-tilelang-programs Benchmark and profile TileLang kernels do_bench backends, TFLOPS/bandwidth, ncu bottleneck diagnosis (pipe utilization, warp stalls), roofline
torch-profiling-tilelang-programs Lightweight torch.profiler alternative to ncu/nsys for TileLang torch.profiler setup, key_averages, chrome trace, roofline classification (IO / CUDA-core / tensor-core), launch overhead, memory profiling
optimizing-tilelang-programs Optimize TileLang kernels for performance Tile sizes, pipeline stages, threads, AutoTuner, epilogue fusion, swizzle, ncu-guided tuning
testing-fwd-bwd-kernels Test kernels with forward and backward passes torch.autograd.Function, compare_backward (not gradcheck), mixed-precision, atomicAdd, attention bwd

Workflow

The skills form a natural progression:

writing → debugging → profiling → optimizing
                          ↓           ↓
            torch-profiling   testing-fwd-bwd (for differentiable ops)
  1. Write a kernel using templates from the writing skill
  2. Debug if it fails to compile or produces wrong results
  3. Profile to measure baseline performance — use profiling-tilelang-programs for the ncu/do_bench workflow, or torch-profiling-tilelang-programs when ncu/nsys aren’t available and you want a quick torch.profiler-based pass
  4. Optimize to improve performance
  5. Test fwd+bwd if the kernel needs gradients

CUDA Skills

General-purpose CUDA development skill for debugging, profiling, and optimizing GPU kernels — independent of any specific framework. Originally from technillogue/ptx-isa-markdown. Includes scraped PTX ISA 9.1, CUDA Runtime API 13.1, and CUDA Driver API 13.1 documentation (640+ markdown files) for grep-based lookup.

Skill Description Key Topics
cuda-programming Debug, profile, and optimize CUDA kernels compute-sanitizer, cuda-gdb, ncu/nsys profiling, NVTX, PTX ISA, coalescing, bank conflicts, inline PTX
View this README on GitHub

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Установка

npx skillfish add sablin39/tilelang-cuda-skills