Agent skill · NVIDIA
tilegym-cutile-python
Expert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks.
What it needs
About 11k tokens when loaded.
What this skill does
cuTile Python Programming Skill You are an expert in cuTile programming, specializing in writing high-performance GPU kernels using cuTile's tile-based programming model. This skill provides comprehensive guidance for creating, debugging, and optimizing cuTile kernels. Overview cuTile is a parallel programming model for NVIDIA GPUs with a Python-based DSL that automatically leverages advanced hardware capabilities like tensor cores. This skill helps you write efficient, correct cuTile code. When to Use This Skill Invoke this skill when you need to: Write cuTile GPU kernels from scratch Convert tensor operations to cuTile implementations Debug or fix cuTile kernel code Optimize cuTile kernels for performance Understand cuTile API and programming patterns Validate cuTile implementations Find and adapt examples from available reference sources Optionally specify when invoking: Target tensor shapes Data types (default: float16) Performance requirements Any special constraints Reference Documentation cuTile Language Specification — <https://docs.nvidia.com/cuda/cutile-python>. Covers the execution model, data and memory models, debugging, compilation, and every public op (load/store, factories, reductions, scans, matmul, selection, math, bitwise, comparisons, atomics, metaprogramming, classes, enums, autotuning). Implementation Guidelines (in the guidelines/ directory): 01implementationlessons.md - Important lessons and implementation rules 02codegenerationrules.md - Specific code generation rules and patterns 03concepts.md - Core concepts: tile size restriction, memory operations, kernel fusion, default rules Examples Before starting any cuTile programming task, always search for existing examples first. TileGym is the primary reference; the packaged examples/ directory complements it for ops TileGym does not yet cover (convolution, pooling, scan, GEMV, 4D matmul, split-k GEMM, groupnorm). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tilegym-cutile-python