China Travel Kit
tczyliu/china-travel-kit
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Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents.
Install Tilegym Converting Cutile To Julia
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .claude/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .claude/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-juliaType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .agents/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .agents/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .cursor/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .cursor/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/tilegym-converting-cutile-to-julia--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .gemini/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .gemini/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-juliaInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .github/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .github/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tilegym-converting-cutile-to-julia .opencode/skills/tilegym-converting-cutile-to-julia && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tilegym-converting-cutile-to-julia" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tilegym-converting-cutile-to-julia into .opencode/skills/tilegym-converting-cutile-to-julia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tilegym-converting-cutile-to-julia", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
tilegym-converting-cutile-to-julia- GitHub stars
- 3.6k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 490 words
- Files
- 18 (incl. scripts, references)
- Skills in repo
- 390
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents.
- Works in 8 steps: Analyze the Python kernel: identify… → Write Julia kernel — julia/kernels/.jl… → Convert kernel signature (see… → …
- Translating cuTile Python kernels to Julia cuTile.jl
- SKILL.md covers Workflow Selection, Architecture, Instructions and ⚠️ Top Pitfalls, plus 3 more sections
- Runs Python and Julia scripts from its folder; calls python
What it does
Tilegym Converting Cutile To Julia is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 24 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `examples/01_add/cutile_python.py`).
It sits in Writing & Content, covering Translation. It works with Python. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Translating cuTile Python kernels to Julia cuTile.jl
- Debugging/optimizing existing Julia cuTile translations
Example prompts
- “Use the tilegym-converting-cutile-to-julia skill to convert cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents”
- “/tilegym-converting-cutile-to-julia”
Requirements
- Python 3
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Analyze the Python kernel: identify patterns, shapes, dtypes, operations
- Write Julia kernel — julia/kernels/.jl with cuTile.jl kernel + bridge function(s)
- Convert kernel signature (see translations/workflow.md Phase 2)
- Convert kernel body (apply references/api-mapping.md + references/critical-rules.md)
- Write Julia test — julia/test/test_.jl using Test stdlib + NNlib.jl for reference
- Register test — add include(...) in julia/test/runtests.jl
- Validate — run the bundled validator: python /scripts/validate_cutile_jl.py
- Test — run julia --project=julia/ julia/test/runtests.jl
What it can do on your machine
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python and Julia, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
julialang.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tilegym Converting Cutile To Julia loads about 1.7k tokens when it runs, and up to ~9.2k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 490 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 490 words, ~1,703 tokens.
Download SKILL.mdSave it as
.claude/skills/tilegym-converting-cutile-to-julia/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.- name
- tilegym-converting-cutile-to-julia
- description
- Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.
- license
- CC-BY-4.0 AND Apache-2.0
- metadata.author
- TileGym Team <TileGym@nvidia.com>
- metadata.tags
- cutile, julia, conversion, gpu, kernel
cuTile Python → cuTile.jl (Julia) Conversion
Convert @ct.kernel Python kernels to Julia function ... end cuTile.jl kernels.
Workflow Selection
- Standard conversion → Full workflow:
translations/workflow.md - Errors (
MethodError,IRError, numerical mismatch) →references/debugging.md - Quick reference →
references/api-mapping.md+references/critical-rules.md - Test patterns →
references/testing.md
Architecture
Julia kernels are standalone — no Python bridge, no pytest integration. The Julia sub-project
lives in julia/ at the repo root with its own Project.toml for dependency management.
julia/ # Self-contained Julia sub-project
├── Project.toml # Dependencies: CUDA.jl, cuTile.jl, NNlib.jl, Test
├── kernels/ # cuTile.jl kernel implementations
│ ├── add.jl # ← Ground-truth: 1D element-wise with alpha scaling (tensor+tensor, tensor+scalar)
│ ├── matmul.jl # ← Ground-truth: 2D tiled MMA, standard Julia layout (M,K)×(K,N)→(M,N)
│ └── softmax.jl # ← Ground-truth: 3 strategies (TMA, online, chunked) using ct.load/ct.store
└── test/ # Julia-native tests (using Test stdlib)
├── runtests.jl # Test runner entry point
├── test_add.jl
├── test_matmul.jl
└── test_softmax.jlGround-truth reference: Always consult julia/kernels/*.jl and julia/test/*.jl for patterns that compile and pass tests. These are the canonical examples of working cuTile.jl code.
Instructions
- Analyze the Python kernel: identify patterns, shapes, dtypes, operations
- Write Julia kernel —
julia/kernels/<op>.jlwith cuTile.jl kernel + bridge function(s) - Convert kernel signature (see
translations/workflow.mdPhase 2) - Convert kernel body (apply
references/api-mapping.md+references/critical-rules.md) - Write Julia test —
julia/test/test_<op>.jlusingTeststdlib +NNlib.jlfor reference - Register test — add
include(...)injulia/test/runtests.jl - Validate — run the bundled validator:
python <skill-dir>/scripts/validate_cutile_jl.py <file.jl> - Test — run
julia --project=julia/ julia/test/runtests.jl
Full conversion checklist with post-conversion verification → translations/workflow.md
⚠️ Top Pitfalls
The most dangerous translation errors. Full rules (17 total) in references/critical-rules.md.
| # | Pitfall | One-line fix |
|---|---|---|
| 1 | ct.full() doesn't exist in Julia | Use fill(val, shape), zeros(T, dims...), or ones(T, dims...) |
| 2 | max(a, b) on tiles → IRError | Use max.(a, b) (broadcast dot) |
| 3 | IRError / MethodError mentioning IRStructurizer | Compiler bug — file upstream with minimal reproducer |
| 4 | ct.launch arg order silently wrong | Args are positional — match kernel signature exactly |
| 5 | ct.load with order — index positions wrong | order remaps BOTH shape AND index (Critical Rule 16) |
Worked Examples
Side-by-side Python → Julia conversions matching the released Julia kernels in julia/kernels/. Each directory contains cutile_python.py (before) and cutile_julia.jl (after).
| # | Example | Key Patterns | When to Reference |
|---|---|---|---|
| 01 | add | 1D ct.load/ct.store, alpha scaling, scalar broadcast, fill/zeros, keyword load/store | Starting point; basic TMA + element-wise patterns |
| 02 | matmul | muladd, TF32 conversion, K-loop with for, 2D swizzle, standard Julia layout, ct.@compiler_options | MMA / tensor core operations |
| 03 | softmax | Persistent scheduling, for loops, gather/scatter, padding_mode, multi-pass | Large-tensor reduction patterns |
These match the released kernels in julia/kernels/ (add.jl, matmul.jl, softmax.jl). The examples are simplified teaching versions — always consult julia/kernels/*.jl for the canonical, tested implementations.
Show full SKILL.md (148 more words)Show less
Reference Documents
| Category | Document | Content |
|---|---|---|
| Workflows | translations/workflow.md | Full conversion workflow with todo list, validation loop, checklist |
| Rules | references/critical-rules.md | 17 Critical Rules for cuTile Python → Julia conversion |
| API | references/api-mapping.md | Python↔Julia bidirectional API mapping + kernel patterns |
| Testing | references/testing.md | Julia-native test patterns, tolerances, failure diagnosis |
| Debugging | references/debugging.md | Julia-specific error diagnosis + IR debug commands |
| Scripts | scripts/validate_cutile_jl.py | Static validation for Julia anti-patterns (run it) |
| Ground Truth | julia/kernels/*.jl + julia/test/*.jl | Actual working implementations in the codebase |
Environment Setup
Prerequisite — Julia: this skill requires the Julia version declared in julia/Project.toml under [compat] julia. If julia --version is missing or older than that, install from the official Julia site at https://julialang.org/install/ following the verified installer instructions for your OS. Resume below once julia --version is compatible.
Then, from the repo root:
# Install Julia dependencies declared in julia/Project.toml
julia --project=julia/ -e 'using Pkg; Pkg.instantiate()'
# Run tests
julia --project=julia/ julia/test/runtests.jlRequirements:
- Julia (minimum version declared in
julia/Project.tomlunder[compat] julia) - CUDA 13.1+ driver
- Blackwell GPU (compute capability 10+)
- Dependencies managed via
julia/Project.toml: CUDA.jl, cuTile.jl, NNlib.jl, Test
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 17 other files (scripts, references) in skills/tilegym-converting-cutile-to-julia of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- examples/01_add/cutile_julia.jl
- examples/01_add/cutile_python.py
- examples/02_matmul/cutile_julia.jl
- examples/02_matmul/cutile_python.py
- examples/03_softmax/cutile_julia.jl
- examples/03_softmax/cutile_python.py
- references/api-mapping.md
- references/critical-rules.md
- references/debugging.md
- references/testing.md
- scripts/validate_cutile_jl.py
- … and 4 more
Open the folder on GitHubat commit 14a98ae
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Questions about Tilegym Converting Cutile To Julia
What does Tilegym Converting Cutile To Julia do?
Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Tilegym Converting Cutile To Julia is an agent skill from NVIDIA/skills, published by the product's own GitHub organization.jl Julia equivalents.
When should I use Tilegym Converting Cutile To Julia?
Tilegym Converting Cutile To Julia fits situations like: translating cuTile Python kernels to Julia cuTile.jl; debugging/optimizing existing Julia cuTile translations.
How do I install Tilegym Converting Cutile To Julia in Claude Code?
Run `npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a claude-code`. Or copy the skill folder (skills/tilegym-converting-cutile-to-julia in NVIDIA/skills) into .claude/skills/tilegym-converting-cutile-to-julia in your project. Claude Code loads it when a task matches its description.
How do I install Tilegym Converting Cutile To Julia in Codex?
Run `npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a codex`. Or copy the skill folder (skills/tilegym-converting-cutile-to-julia in NVIDIA/skills) into .agents/skills/tilegym-converting-cutile-to-julia in your project. Codex loads it when a task matches its description.
Can I use Tilegym Converting Cutile To Julia in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tilegym-converting-cutile-to-julia, .gemini/skills/tilegym-converting-cutile-to-julia, .github/skills/tilegym-converting-cutile-to-julia and .opencode/skills/tilegym-converting-cutile-to-julia in your project.
What does Tilegym Converting Cutile To Julia need to run?
Going by SKILL.md and its folder, Tilegym Converting Cutile To Julia needs Python and Julia for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Tilegym Converting Cutile To Julia access the network?
SKILL.md names 1 domain. As links in the text: julialang.org. This is read from the text; nothing was executed.
Is Tilegym Converting Cutile To Julia safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Tilegym Converting Cutile To Julia use?
Tilegym Converting Cutile To Julia is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tilegym Converting Cutile To Julia use?
About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.5k tokens, read only when the agent opens those files.
What are the alternatives to Tilegym Converting Cutile To Julia?
Skills that share tags, products or a category with Tilegym Converting Cutile To Julia: China Travel Kit (tczyliu/china-travel-kit, 194 stars), Technology Search (freestylefly/wesight, 946 stars), Translate Po (python/python-docs-zh-tw, 284 stars) and Obs Build Logs (Nuitka/Nuitka, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tilegym Converting Cutile To Julia?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.