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SkillAtlasSkill 详情
nv-segment-ct-finetune
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.
其他
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：3 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/nv-segment-ct-finetune" 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/nv-segment-ct-finetune" 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/nv-segment-ct-finetune" 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/nv-segment-ct-finetune" 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/nv-segment-ct-finetune" 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: nv-segment-ct-finetune
description: Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels. Not for clinical validation.
license: Apache-2.0
allowed-tools: Bash
metadata:
author: NVIDIA MedTech Team
tags:
- MedTech
- CT
- finetuning
- segmentationNV-Segment-CT Finetune
Purpose
- Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels. Not for clinical validation.
- Wraps the upstream MONAI bundle entrypoint; do not replace it with handwritten training or inference code.
- Manifest inputs are
dataset_dir,datalist,target_anatomy,label_mapping,smoke,sanity,auto_seg, andskip_formal_eval. - Manifest outputs are
finetuned_ckptand schema-checkedresult_json.
Instructions
- Run
scripts/run_finetune.py; do not patch files underbundle/or upstream checkouts during normal skill use. - For standalone Bash, include the fresh-environment setup line before the wrapper; benchmark venvs start empty.
- Run the committed script in place from the repo root. Do not copy this skill to a runtime directory, and do not use
rmor cleanup commands in generated invocations. - If a host exposes
run_script, userun_script("scripts/run_finetune.py", args=[...]); otherwise run from the repo root. - For the shortest workflow check, use
--smoke; for MSD Task06 Lung Tumor reproduction, use--sanity. - Read
references/task06-and-results.mdonly when you need Task06 reference details, output-field definitions, or manual bundle setup notes.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/run_finetune.py | Primary entrypoint declared by skill_manifest.yaml; stages configs, runs MONAI, and writes output.json. | [FIXTURE_OR_DATASET] --output-dir OUT_DIR [--smoke] [--sanity] [--auto-seg] [--dataset-dir DIR] [--datalist JSON] [--target-anatomy TEXT] [--label-mapping JSON] [--patch-size JSON] |
Prerequisites
- Python 3.10+ with CUDA-capable Torch for GPU runs.
- Runtime packages from
skill_manifest.yaml, especiallymonai==1.4.0,numpy<2,nibabel,scipy,typer,PyYAML,fire,pytorch-ignite,einops, andhuggingface_hub. - Optional environment variables:
CUDA_VISIBLE_DEVICESrestricts visible GPUs;NPROC_PER_NODEoverrides GPU count and values>=2select multi-GPU mode for non-sanity runs. - Side effects: writes generated bundle configs under
skills/nv-segment-ct-finetune/bundle/configs/, includingskills/nv-segment-ct-finetune/bundle/configs/auto_override.json,skills/nv-segment-ct-finetune/bundle/configs/train_continual_task06_lung.json, andskills/nv-segment-ct-finetune/bundle/configs/dfw_no_logging.json; writes checkpoints/evidence under--output-dir, may cache model assets under~/.cache/huggingface/, and may contacthttps://huggingface.coorhttps://raw.githubusercontent.com.
Fresh environment setup:
python -m pip install "monai==1.4.0" "numpy<2" pytorch-ignite einops nibabel scipy typer PyYAML fire huggingface_hub
Known upstream compatibility constraints:
- DFW Task06 reference: Python
3.10.16, MONAI1.4.0, Torch2.7.0+cu126. - Use exact
monai==1.4.0for smoke, sanity, and evidence runs; MONAI 1.5.x can crash the upstream finetune loss on boolean labels. - Do not float the dependency as
monai>=1.4,<1.6in generated commands.
Usage
Smoke-scale workflow check:
python -m pip install "monai==1.4.0" "numpy<2" pytorch-ignite einops nibabel scipy typer PyYAML fire huggingface_hub && \
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
PATH_TO_DATASET \
--smoke \
--patch-size '[64,64,64]' \
--output-dir runs/nvseg_smoke
Use the staged dataset as PATH_TO_DATASET. For the micro fixture, use skills/nv-segment-ct-finetune/fixtures/spleen_micro. Smoke mode proves wiring, config generation, checkpoint loading, and runtime compatibility; it is not a quality bar.
MSD Task06 Lung Tumor sanity reproduction:
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
/path/to/Task06 \
--sanity \
--output-dir runs/nvseg_task06_sanity
The sanity preset follows the single-GPU DFW recipe: fold-0 validation, label mapping [[1, 23]] for lung tumor, automatic class-prompt segmentation, patch [128,128,128], 5 epochs, and original-spacing configs/evaluate.json scoring before and after training. Expected reference range is pretrained Dice about 0.6697, training-best Dice about 0.6905, and fine-tuned formal Dice about 0.6836.
User-data finetune:
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
--dataset-dir /path/to/dataset \
--datalist /path/to/datalist.json \
--target-anatomy "lung tumor" \
--auto-seg \
--epochs 5 \
--patch-size '[128,128,128]' \
--output-dir runs/nvseg_user_finetune
Use --label-mapping '[[1, 23]]' when local label values are custom or the anatomy name is ambiguous.
Examples
Smoke run on a staged tiny dataset:
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
runs/with_vs_without_nv/_inputs/nv_segment_ct_finetune/input_dataset \
--smoke \
--patch-size '[64,64,64]' \
--output-dir runs/nvseg_smoke
Task06 sanity run on a local MSD cache:
python skills/nv-segment-ct-finetune/scripts/run_finetune.py \
.workbench_data/datasets/Task06_Lung \
--sanity \
--output-dir runs/nvseg_task06_sanity
Data Contract
- Preferred layout:
dataset/imagesTr/*.nii.gzanddataset/labelsTr/*.nii.gz. - Labels must align one-to-one with images by basename.
- The target label value must be present in the training labels.
- Use a datalist when patient-level splitting matters. The bundle default
foldis0, sofold: 0entries are validation and all other folds are training. - Every trained foreground label must map to an existing VISTA3D global class id from
bundle/label_dict.json; this skill cannot invent a new class.
Results
Check output.json in the run directory first:
formal_pretrained_val_diceandformal_finetuned_val_dice: original-spacing pre/post scores when formal eval is enabled.training_start_val_dice,val_dice_per_epoch, andtraining_best_val_dice: training-time validation trace.finetuned_ckpt_matches_pretrained_weights: detects the epoch-0 checkpoint trap whenval_at_start=true.recommended_ckpt: checkpoint to keep. Do not blindly use the last epoch ormodel_finetune.pt.runtime.oom,runtime.peak_gpu_mb, and phase logs: distinguish OOM, slow validation, and process failure.
Decision rule: prefer formal original-spacing pre/post scores when present; reject tensor-identical "fine-tuned" checkpoints for sanity recovery; treat improved: false as valid evidence rather than a wrapper failure.
Limitations
- Thin wrapper. Training, validation, transforms, and checkpointing are delegated to the upstream bundle in
bundle/. - Reproduction record only: the successful five-epoch Task06 run used Python
3.12.3, PyTorch2.12.0+cu130with CUDA13.0, MONAI1.4.0, NumPy1.26.4, PyTorch-Ignite0.5.4, NiBabel5.4.2, SciPy1.16.0, einops0.8.2, Fire0.7.1, Hugging Face Hub0.36.2, Transformers4.57.6, Typer0.25.1, PyYAML6.0.3, and MLflow3.14.0on one NVIDIA RTX 6000 Ada 48 GB GPU. These versions document the evidence environment; they are not additional package constraints or a claim that other versions cannot work. - The auto-derived plan is heuristic; caller-provided
--patch-size,--cache-rate,--epochs, and--learning-ratewin. - The Task06 sanity recipe intentionally forces single-GPU execution to match the DFW reference. Multi-GPU mode for other datasets requires host
torchrunsupport. - The paired verifier is CPU-only and audits the evidence pack; it does not re-run GPU segmentation.
- Not for clinical deployment, clinical interpretation, autonomous diagnosis, or regulatory submission.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime drift from skill_manifest.yaml. | Install the packages above or use the documented environment. |
| Low Task06 pretrained Dice | Wrong config, wrong checkpoint, data split drift, or dependency drift. | Compare environment fields and staged configs before changing training logic. |
model_finetune.pt matches pretrained | val_at_start=true selected epoch 0 as best. | Use recommended_ckpt; treat sanity recovery as failed unless a changed checkpoint improves formal Dice. |
| Missing formal Dice fields | Formal eval failed or was skipped. | Inspect eval_pretrained.log, eval_finetuned.log, and metrics.csv. |
| GPU out of memory | Patch/cache settings too large. | Reduce --patch-size, lower --cache-rate, or reduce workers. |
| No validation cases | Datalist lacks fold: 0. | Provide at least one validation entry. |
Verification
Run the implemented verifier when quality gates matter:
python -m eval_engine.run_trusted skills/nv-segment-ct-finetune \
--fixture skills/nv-segment-ct-finetune/fixtures/spleen_micro \
--out runs/nvseg_trusted
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