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tilegym-adding-cutile-kernel
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 80Security 80
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
测试与质量DevOps 与部署
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 未检测到明显外部权限要求。
- 未检测到高风险命令。
- 扫描发现：1 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tilegym-adding-cutile-kernel" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tilegym-adding-cutile-kernel" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tilegym-adding-cutile-kernel" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tilegym-adding-cutile-kernel" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tilegym-adding-cutile-kernel" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: tilegym-adding-cutile-kernel
description: Add a new cuTile GPU kernel operator to TileGym. Covers dispatch registration in ops.py, cuTile backend implementation, __init__.py exports, test creation, and benchmark in tests/benchmark. Use when adding, creating, or implementing a new cuTile operator/kernel in TileGym, or when asking how to register a new cuTile op.
license: CC-BY-4.0 AND Apache-2.0
metadata:
author: "TileGym Team <TileGym@nvidia.com>"
tags:
- cutile
- kernel
- tilegym
- gpu
- dispatchAdding a cuTile Kernel to TileGym
End-to-end workflow for adding a new operator (e.g., my_op) with cuTile backend.
Execution Rules
MUST follow these rules strictly:
- Use TodoWrite to create the checklist below BEFORE writing any code
- Execute steps in order — do NOT skip ahead or combine steps
- Mark each todo as
completedafter finishing,in_progresswhen starting - If a step is not applicable (e.g., no cuTile impl), mark it
completedwith a note, do NOT silently skip - Each step MUST result in a file write or explicit skip decision — no silent omissions
Instructions
MUST copy this checklist to TodoWrite at the start:
- [ ] Step 1: Register dispatch interface in ops.py
- [ ] Step 2: Implement cuTile backend
- [ ] Step 3: Register in __init__.py (cutile)
- [ ] Step 4: Add tests
- [ ] Step 5: Add benchmark to tests/benchmark
- [ ] Step 6: Verify (run pytest + lint)
Step 1: Register dispatch interface
File: src/tilegym/ops/ops.py
Add a @dispatch function — this is the single entry point for all backends.
@dispatch(
"my_op",
)
def my_op(
input: torch.Tensor,
out: Optional[torch.Tensor] = None,
**kwargs: Any,
):
"""
Description of my_op.
Args:
input: Input tensor
out: Optional preallocated output tensor
**kwargs: Additional arguments for backend-specific configurations
Returns:
torch.Tensor
"""
raise NotImplementedError(f"my_op is not implemented for {get_current_backend()}")
Key rules:
- Function body only raises
NotImplementedError - Include
**kwargsfor backend-specific parameters
Reference: See existing ops in src/tilegym/ops/ops.py (e.g., silu_and_mul, softmax)
Step 2: Implement cuTile backend
File: src/tilegym/ops/cutile/my_op.py
The file structure follows this template:
import torch
import cuda.tile as ct
from tilegym.backend import register_impl
@ct.kernel
def my_op_kernel_ct(x, output, n_elements: ct.Constant[int], BLOCK_SIZE: ct.Constant[int]):
bid = ct.bid(0)
indices = bid * BLOCK_SIZE + ct.arange(0, BLOCK_SIZE)
x_val = ct.gather(x, indices)
# ... compute ...
ct.scatter(output, indices, result)
@register_impl("my_op", backend="cutile")
def my_op(input: torch.Tensor, out: torch.Tensor = None, **kwargs) -> torch.Tensor:
n = input.numel()
if out is None:
out = torch.empty_like(input)
grid = ((n + 1023) // 1024,)
ct.launch(stream, grid, kernel, (some args, ...))
return out
Reference: src/tilegym/ops/cutile/silu_and_mul.py
Step 3: Register in __init__.py (CRITICAL)
Missing this step means the cuTile backend implementation never gets loaded.
File: src/tilegym/ops/cutile/__init__.py
Add inside if is_backend_available("cutile"): block (alphabetically):
from . import my_op
And in the function import section:
from .my_op import my_op
And add "my_op" to __all__.
Step 4: Add tests
File: tests/ops/test_my_op.py
CRITICAL: Always import from tilegym.ops, NEVER from tilegym.ops.cutile.my_op.
import pytest
import torch
from tilegym.backend import is_backend_available, set_backend
from .. import common
_backends = ["cutile"]
class Test_MY_OP(common.PyTestCase):
@staticmethod
def reference(input):
"""Reference implementation using PyTorch."""
return torch.some_reference(input)
@pytest.mark.parametrize("shape, dtype", [
((1024,), torch.float16),
((1024, 512), torch.float32),
((64, 64, 64), torch.bfloat16),
])
@pytest.mark.parametrize("backend", _backends)
def test_op(self, shape, dtype, backend, arch):
if backend == "cutile" and not is_backend_available("cutile"):
pytest.skip("Cutile backend not available")
try:
set_backend(backend)
except Exception as e:
pytest.skip(f"Backend is not supported: {e}")
self.setUp()
from tilegym.ops import my_op
A = torch.randn(*shape, dtype=dtype, device="cuda")
self.assertCorrectness(
my_op, self.reference, {"input": A},
atol=1e-3, rtol=1e-3,
)
Key patterns:
_backends = ["cutile"]test_op: useset_backend(backend)with try-except, callself.setUp()
Reference: tests/ops/test_silu_and_mul.py
Below is the common errors.
1. Missing _backends list (inside class)
2. test_op / test_op_xxx — missing @pytest.mark.parametrize("backend", _backends), backend parameter, and tilegym.is_backend_available / tilegym.set_backend pattern
Step 5: Add benchmark to tests/benchmark
File: tests/benchmark/bench_my_op.py
Key rules from benchmark_rules.md:
- Call the op via
tilegym.ops.my_op(a, b, ..., backend=backend)— do not useset_backend. - Define
ALL_BACKENDS(include at leastcutileandtorch), filter withget_supported_backends(). - Implement
reference_my_op(...)and register it:register_impl("my_op", "torch")(reference_my_op). - Use
create_benchmark_config()to buildtriton.testing.Benchmarkconfigs (e.g. by shape/dtype). - Use
@triton.testing.perf_report([...])onbench_my_op(...); inside the bench function: correctness check withtorch.testing.assert_close(fn(), ref(), ...), thenms = triton.testing.do_bench(fn)(ordo_bench_cudagraph), compute GB/s or TFLOPS, and return the metric. - Entry point:
if __name__ == "__main__": bench_my_op.run(print_data=True).
Template structure:
import torch
import triton
import triton.testing
import tilegym
from tilegym.backend import is_backend_available, register_impl
ALL_BACKENDS = [
("cutile", "cuTile", ("orange", "-")) if is_backend_available("cutile") else None,
("torch", "PyTorch", ("green", "-")),
]
def get_supported_backends():
return [p for p in ALL_BACKENDS if p is not None]
def reference_my_op(input: torch.Tensor, out: torch.Tensor = None, **kwargs):
"""Reference implementation using PyTorch."""
...
register_impl("my_op", "torch")(reference_my_op)
def create_benchmark_config(datatype, ...):
available_backends = get_supported_backends()
if not available_backends:
return None
backends, names, styles = zip(*available_backends)
return triton.testing.Benchmark(
x_names=["M"], # or other dimension names
x_vals=[...],
line_arg="backend",
line_vals=list(backends),
line_names=list(names),
styles=list(styles),
ylabel="GB/s", # or TFLOPS
plot_name="my-op-...",
args={"datatype": datatype, ...},
)
@triton.testing.perf_report([
create_benchmark_config(datatype, ...)
for datatype in [torch.float16, torch.float32]
for ... in [...]
])
def bench_my_op(M, backend, datatype, ..., device="cuda"):
x = torch.randn(..., dtype=datatype, device=device)
fn = lambda: tilegym.ops.my_op(x, backend=backend)
ref = lambda: reference_my_op(x)
torch.testing.assert_close(fn(), ref(), rtol=1e-2, atol=1e-2)
ms = triton.testing.do_bench(fn) # or do_bench_cudagraph(fn)
# Compute metric (e.g. GB/s or TFLOPS) from ms and problem size
return metric
if __name__ == "__main__":
bench_my_op.run(print_data=True)
Benchmark Plot Names: Must include -TFLOPS or -GBps suffix
- Example:
plot_name=f"persistent-layer-norm-M{num_rows}-{dtype_name}-GBps"
Step 6: Verify
# Run tests
pytest tests/ops/test_my_op.py -v
# Run benchmark (optional)
python tests/benchmark/bench_my_op.py
# Lint
pre-commit run -a
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