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/tilegym-cutile-python

@129a108
by NVIDIA Corporationnvidia/skills3.5k stars
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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.

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Use this Skill: https://skilld.dev/gh/nvidia/skills/tilegym-cutile-python

This session only. Nothing lands on disk.

examplestilegym_and_examples_guide.md

≈838 tokens on demand. Your agent reads this file only when SKILL.md points to it.

TileGym and Packaged Examples Guide

Always look at existing cuTile code before writing a new kernel. There are two sources, in priority order: TileGym's own ops (primary), then the skill's packaged examples/ (complementary, for ops TileGym does not yet cover).

Locating TileGym

The skill supports two installation contexts. Figure out which one applies before searching.

Case 1 — skill inside a TileGym checkout

Path looks like <repo>/skills/tilegym-cutile-python/ (or <repo>/.agents/skills/tilegym-cutile-python/ / <repo>/.claude/skills/tilegym-cutile-python/ via the backward-compat symlinks). The enclosing repo is TileGym. No clone needed — use it directly:

<repo>/src/tilegym/ops/cutile/

Case 2 — skill installed elsewhere (e.g. ~/.agents/skills/ or ~/.claude/skills/)

Path looks like ~/.agents/skills/tilegym-cutile-python/ or ~/.claude/skills/tilegym-cutile-python/, or the skill is inside some other repo that does not ship src/tilegym/. TileGym is not adjacent; clone it once on first use to the cache directory and use it from there:

${TILEGYM_SKILL_CACHE_DIR:-~/.cache/tilegym}/TileGym/src/tilegym/ops/cutile/

Clone URL: https://github.com/NVIDIA/TileGym.git.

Matching the cache to your cuda-tile version. Read the installed cuda-tile version — cuda.tile.__version__ or pip show cuda-tile. In the cached TileGym checkout, pick the tag whose version matches the same MAJOR.MINOR; if several patch tags share that MAJOR.MINOR, use the highest. Deterministic fallback when no tag matches MAJOR.MINOR: pick the most recent tag with the same MAJOR; only fall back to main as a last resort (API mismatches are possible). Refresh the cache whenever cuda-tile is upgraded.

How to decide

Starting from the skill directory, walk up looking for a src/tilegym/ sibling. If you find one, you are in Case 1 — use it. Otherwise you are in Case 2 — use (or create) the cached checkout.

TileGym contents (src/tilegym/ops/cutile/)

Production cuTile kernels, autotuned and perf-tuned: standard GEMM/BMM (matmul.py, bmm.py, group_gemm.py), attention variants (attention.py, flash_attention.py, mla*.py, pod_attention.py, gemma_attention*.py), normalization (layer_norm.py, rms_norm.py, cache_layer_norm.py), activations (activation/*.py, swiglu.py, silu_and_mul.py), RoPE, dropout, MoE, FFT, transpose, and more. This is the canonical reference.

Packaged examples (<skill_dir>/examples/)

Complementary — covers ops TileGym does not yet implement. These prioritize correctness over performance; tune block sizes and validate against a PyTorch reference before using.

Directory Operations Covered
examples/convolution/ conv2d, conv3d, conv_transpose_2d, conv_transpose_3d
examples/matmul/ gemv, matmul_4d, split_k_gemm
examples/normalization/ group_norm
examples/pooling/ maxpool3d, avgpool3d
examples/scan/ cumsum, cumprod

Search order

  1. Search TileGym's src/tilegym/ops/cutile/ for the op. Read the closest match and adapt.
  2. If TileGym has no match, search the skill's packaged examples/.
  3. If neither has it, consult the language spec at https://docs.nvidia.com/cuda/cutile-python and design from scratch.

Source: SKILL.md on GitHub

Third-party checks

1 warning3mo3 checks · Risk SAFE
  • Gen Agent Trust Hub3mo

    The skill is a cuTile programming assistant provided by NVIDIA. It guides agents through writing, optimizing, and validating GPU kernels. Security analysis found that it clones official repositories from GitHub (NVIDIA TileGym and PyTorch) and executes generated scripts as part of its validation workflow. Both behaviors are standard for its intended use case. Static analysis warnings for eval() calls were confirmed as false positives, as they refer to the PyTorch .eval() method rather than the Python eval() function.

  • Socket3mo

    No alerts

  • Snyk3mo

    Risk: MEDIUM · 2 issues

Provenance

Signed by skilld at 129a108. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 4 months ago

Capability

version
1.3.0
Other metadata
metadata
{
  "author": "TileGym Team <TileGym@nvidia.com>",
  "tags": [
    "cutile",
    "gpu-kernels",
    "cuda"
  ]
}

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