tilegym-converting-cutile-to-julia

tilegym-converting-cutile-to-julia is a skill for Claude Code, Codex from NVIDIA/TileGym. It costs 97 tokens per session (2,103 once invoked), scanned A, original, no licence file.

A conversion guide for translating cuTile Python GPU kernels into cuTile.jl code. Julia is a programming language, so the guide accounts for differences in indexing, broadcasting, memory layout, types, and kernel launches.

In plain words
What is it for?
Porting cuTile kernels to Julia, including changing zero-based indexing, row-major data handling, types, broadcasts, and launch calls.
Why use it?
It addresses language and data-layout differences that can make a direct translation incorrect or change how the GPU kernel runs.

Skill for Claude CodeCodex ✓ vendor

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Porting cuTile kernels to Julia, including changing zero-based indexing, row-major data handling, types, broadcasts, and launch calls.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nvidia/tilegym/tilegym-converting-cutile-to-julia
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add NVIDIA/TileGym --skill tilegym-converting-cutile-to-julia
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/TileGym

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for tilegym-converting-cutile-to-julia

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia/github.svg)](https://agentmods.dev/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia)
Your own site
<a href="https://agentmods.dev/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia"><img src="https://agentmods.dev/badge/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for tilegym-converting-cutile-to-julia

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia"><img src="https://agentmods.dev/badge/skills/nvidia/tilegym/tilegym-converting-cutile-to-julia.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,103 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00097 $0.02103
Opus 5.5 $0.00039 $0.00841
Sonnet 5.5 $0.00019 $0.00421
Haiku 4.5 $0.00010 $0.00210

Measured 8d ago against content hash 2f8d0329cfe8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tilegym-converting-cutile-to-julia scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/01_add/cutile_python.py, examples/02_matmul/cutile_python.py, examples/03_softmax/cutile_python.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/tilegym-converting-cutile-to-julia/SKILL.md · 125 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago Changed · +10 lines 2f8d0329cfe8
  2. 2mo ago First seen · 115 lines · 97 tokens per session scan A 2ab5d1dd79b2

Subscribe to this mod's changes

tilegym-converting-cutile-to-julia is a skill published in the GitHub repository NVIDIA/TileGym (834 stars, last pushed yesterday), with no licence file. It adds 97 tokens to every session and 2,103 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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rocm-kernelshuggingface/kernelsSkill783yesterdayApache-2.0A93Claude Code
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