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SkillAtlasSkill 详情
tilegym-improve-cutile-kernel-perf
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.
测试与质量数据与 AI
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-improve-cutile-kernel-perf" 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-improve-cutile-kernel-perf" 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-improve-cutile-kernel-perf" 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-improve-cutile-kernel-perf" 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-improve-cutile-kernel-perf" 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-improve-cutile-kernel-perf
description: Iteratively optimize cuTile kernel performance through systematic profiling, bottleneck analysis, IR comparison, and targeted tuning. Covers tile sizes, occupancy, autotune configs, TMA, latency hints, persistent scheduling, num_ctas, flush_to_zero, and IR-level debugging. Use when asked to "optimize cutile kernel", "improve kernel perf", "tune cutile performance", "make kernel faster", or iteratively benchmark and refine a cuTile GPU kernel in the TileGym project.
license: CC-BY-4.0 AND Apache-2.0
metadata:
author: "TileGym Team <TileGym@nvidia.com>"
version: "2026.4.11"
environment: "IDE: Claude Code, Cursor (Agent mode); model: Opus 4.6"
requires: "GPU node Blackwell, Hopper and Ampere for benchmarking"
tags:
- cutile
- performance
- optimization
- kernel
- profilingIterative cuTile Kernel Performance Optimization
Systematically profile, diagnose bottlenecks, and iteratively tune a cuTile kernel's performance in the TileGym repository.
Instructions
Follow the three phases in order: Setup the environment and baseline, run the Experimentation loop with a tracked log, then iterate The experiment loop until perf goals are met or further gains plateau.
Setup
Work with user to prepare optimization environment:
-
Create a fresh git branch: Propose a branch name, e.g.,
cutile-perf-<kernel_name>-<date>from current branch. Checkoutgit checkout -b <branch name> -
Locate the target kernel:
- cuTile kernels live under
src/tilegym/suites/<suite>/cutile/orsrc/tilegym/ops/cutile/ - Read the kernel file and identify: the
@ct.kerneldecorated function(s), the launch wrapper (ct.launch()orct_experimental.autotune_launch()), the@register_implregistration, and current autotune configs (if any)
- cuTile kernels live under
-
Classify the kernel:
- Arithmetic Intensity < 10 -> Memory-bound
- Arithmetic Intensity 10-50 -> Balanced
- Arithmetic Intensity > 50 -> Compute-bound
Note: classification is only used to pick the optimization priority order in the experiment loop. The core metric is always
latency (ms). -
Check GPU environment:
- Ensure a GPU node (Blackwell or Ampere GPU) is available
- All subsequent benchmark commands should run on the GPU node
-
Study related references:
references/optimization-playbook.md: Step-by-step recipes for each optimization (A through J) with before/after code examplesreferences/perf-knobs-catalog.md: Complete catalog of all tunable parameters (TMA, persistent scheduling, occupancy, tile sizes, latency hints, etc.)references/cutile-api-reference.md: cuTile API reference and 18 critical rulesreferences/performance-model.md: Roofline/performance model, bottleneck diagnosis, autotuningreferences/ir-dump-guide.md: IR dump, analysis, and error diagnosisreferences/cutile-patterns-reference.md: Common cuTile patterns and conversion quick-reference
-
Create @sandbox/perf_results.md to track progress. The first run will write a baseline
-
Confirm and go: Once you get confirmation, kick off the experimentation
Experimentation
Every experiment iteration applies ONE optimization to the target kernel, verifies correctness, re-benchmarks, and records results. Each iteration should be enforced to finish within 10 minutes.
The goal
- Improve the core metric: reduce
latency (ms) - Subject to the core constraint: Correctness shall not regress — every optimization MUST preserve numerical correctness.
latency (ms)shall not regress > 2% compared to baseline.
What you can change
- The target kernel file under
src/tilegym/suites/<suite>/cutile/orsrc/tilegym/ops/cutile/: kernel body, tile sizes, occupancy, num_ctas, TMA usage, latency hints, flush_to_zero, autotune configs, persistent scheduling, and other cuTile-specific parameters - The kernel's launch wrapper: grid computation, autotune config space
- @sandbox/: Feel free to add new files or modify files created by you, but don't check to git
What you can NOT change
- Kernel functional semantics (inputs, outputs, and numerical behavior within tolerance)
- Test infrastructure and benchmark harness
- Anything not listed above
What to expect from experiment outputs
Correctness test:
python -m pytest tests/suites/.../test_<kernel_name>.py -k "test_ and cutile and not test_perf" -v
Performance benchmark:
For each iteration:
- Run pytest benchmark:
python -m pytest ... --print-record→ extract latency (ms) - Record latency in perf_results.md
Benchmark cmdlines:
python -m pytest tests/suites/.../test_<kernel_name>.py -k "test_perf and cutile" --print-record -v
latency sample:
Cutile: {'forward': {'mean': 3.7903138461538455, 'std': 0.0016941310873207053, 'rel_std': 0.044696327430505396, 'median': 3.789880999999999, 'min': 3.7883389999999992, 'max': 3.7941230000000004, 'nrep': 13, 'peak_mem_mb': 913}} ms
Track experiment progress
Use @sandbox/perf_results.md to record each iteration's results. It should only contain a Markdown table with 5 columns:
iteration: iteration number, starting from 0 (baseline)optimization: what was applied (e.g., "baseline", "TMA replace gather", "persistent scheduling")latency_ms: kernel latency in milliseconds, six decimal pointscorrectness: PASS or FAILstatus: Whether this iteration waskeep,revert, orcrash
Example content:
| iteration | optimization | latency_ms | correctness | status |
|----------:|:-------------------|-----------:|:------------|-------:|
| 0 | baseline | 0.820000 | PASS | keep |
| 1 | TMA replace gather | 0.390000 | PASS | keep |
Create the tabular header if the file was empty. Append one line for each iteration.
The baseline
The first iteration (iteration 0) will not change any code and simply run the correctness test and performance benchmark. Results will be listed at the first row as baseline.
The experiment loop
Core methodology is to apply ONE optimization per iteration from the playbook, verify correctness, benchmark, and decide whether to keep or revert. Try one optimization at a time, and have clean experiment records.
LOOP:
-
Check git status: Current git branch/commit we're on
-
Select and apply ONE optimization from
references/optimization-playbook.md: -
Verify correctness — if fails, revert immediately. Common causes:
flush_to_zero/rounding_mode=APPROXchanged results, tile size OOB,allow_tma=Falsesemantics, persistent loop bound error -
Re-benchmark and compare against current baseline
-
Git commit
-
Record results to @sandbox/perf_results.md
-
Decision rules:
Outcome Action Improvement( latency (ms)) >= 5%Accept as new baseline, continue Improvement 2-5% Accept, lower priority for next iteration Improvement < 2% Accept but stop unless user wants more Regression on any config Revert immediately, try next optimization No improvement after 2 consecutive iterations Stop Root cause is schedulingorunknownEscalate to user -
If keeping, advance the baseline numbers and continue loop
-
If reverting, git reset back to where you started and try the next optimization in priority order UNTIL: all attempts are finished, or more than 25 iterations have occurred, or the user interrupts
Be autonomous: Ask user clarifications at setup phase. Once stepped into the experiment loop, do not pause to ask user feedback: Use your best judgement for decision making, consult the optimization playbook and perf knobs catalog promptly, and think harder if stuck.
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