复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
SkillAtlasSkill 详情
paidf-curation-and-retrieval
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 70
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.
研究与检索
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。
- 可能需要外部 token、网络权限或第三方服务。
- 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/paidf-curation-and-retrieval" 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/paidf-curation-and-retrieval" 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/paidf-curation-and-retrieval" 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/paidf-curation-and-retrieval" 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/paidf-curation-and-retrieval" 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: paidf-curation-and-retrieval
description: >-
Use when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator
pipelines (split, filter, caption, embed, dedup, shard, image annotate) or
PAIDF Data Mining nearest-neighbor matching on Curator embeddings. Activate
for Make or CLI pipeline config, GPU run preflight, FFmpeg sidecar, SAM3
keys, or Curator-to-TAO handoff. Do not use for generic ETL, vector-database
RAG, model training, orchestration, or embeddings outside Cosmos Curator
and PAIDF Data Mining.
license: CC-BY-4.0 AND Apache-2.0
owner: NVIDIA
service: physical-ai-data-factory
reviewed: 2026-09-01
metadata:
author: "NVIDIA <opensource@nvidia.com>"
version: 1.1.0
tags:
- data-curation
- dataset-retrieval
- cosmos-curator
- tao
- physical-aiPAIDF Curator Operator Skill
GPU-accelerated video and image curation via NVIDIA Cosmos Curator inside
Physical AI Data Factory — Curation and Retrieval
(paidf-curation-and-retrieval). This skill is a short Curator index.
Embedding handoff boundaries live in
data-mining.md and
curation-retrieval-workflow.md;
mining execution is make help and the repository cookbooks.
- Video:
split,dedup,shard. - Image:
annotate(load → filter → embed → caption → write). - Handoff: Curator IV2 or CE1 parquet that downstream mining can consume. See data-mining.md and curation-retrieval-workflow.md.
Purpose
Turn raw video and image collections into curated, training-ready datasets. This skill configures and runs cosmos-curator pipelines (clip splitting, filtering, captioning, embeddings, SAM3 event verification, dedup, WebDataset sharding, image annotate) and supports KPI-driven, distribution-aware, and restrictive curation.
Instructions
- Classify the request as advisory, config, run, or TAO handoff. Do not mix those routes.
- For config work, complete the mandatory pre-flight below before writing YAML.
- For an explicit run, load running-pipelines.md, validate the config, obtain credentials only through approved injection, then execute after authorization.
- For a TAO handoff, validate Curator output and the declared embedding family using data-mining.md before mining.
- Return the Output Format below. Never print secret values.
Examples
- Advisory: "How much SHM should I set?" → read running-pipelines.md and report guidance. Do not run Docker.
- Config with no KPI: complete the calibration interview in calibration-config.md, then emit YAML.
- Run: after sample clips are staged,
make run-pipelinewith the trafficsplit-minimalcookbook recipe only after preflight and user authorization. - FFmpeg missing in the container: install the host sidecar
(
make ffmpeg-install) per ffmpeg-sidecar.md.
Inputs
Required inputs depend on the route:
- Advisory request: the question plus relevant repository and config context.
- Config request: input and output locations, domain and goal, available KPI output or representative samples, and hardware constraints. If neither KPI output nor samples exist, complete the calibration interview before writing YAML.
- Run request: reviewed config path, data and model paths, runtime, GPU, and SHM constraints, and explicit authorization to execute.
- TAO handoff: validated artifact paths and declared embedding family.
Optional inputs include target distributions, event taxonomy, prompt choices, existing output metadata, and user-approved operational constraints.
Resolve inputs in this order: repository configuration and validated run artifacts; explicit prompt arguments and corrections; available agent context; then the broad user prompt. Explicit user instructions remain authoritative unless unsafe or incompatible, in which case stop and explain the conflict. Never infer secret values: credentials come only from approved runtime injection.
Prerequisites
- GPU host with NVIDIA drivers +
nvidia-container-toolkit; Docker. SHM sized from host RAM (SHM_SIZE, default 24gb). cosmos-curatorimage:make pulluses the pin configured by the example env file and Make. No separate product engine image. Source builds are developer-only; see cosmos-curator.md.- FFmpeg host sidecar for distributable images (
make ffmpeg-install) — they do not bundle FFmpeg. See ffmpeg-sidecar.md. - Credentials as needed: inject S3 and captioning API keys at runtime through an approved secret manager or operator deployment mechanism. Never put secret values in repository files, commands, logs, or examples. The env file is for non-secret image and CDS profile overrides copied from the example env file.
Mandatory pre-flight: do NOT emit a pipeline config without context
Before writing any *.yaml pipeline config, the agent MUST verify
that one of the following is true:
- KPI run output exists -- read it and use distribution-analysis.md, distribution-aware-curation.md, and configuration-decision-tree.md.
- KPI sample videos are available for inspection / discovery -- follow context-understanding.md Phase 1.
- No KPI of any kind -- no baseline exists. Read calibration-config.md and complete its Phase 1 interview (Inputs / Domain / Goal / Hardware / Calibration) BEFORE emitting a config. The interview is binding, not advisory.
If the user requests a config with only a one-line description ("configure cosmos-curator for my videos"), assume the calibration workflow and ask the Phase 1 interview questions in one batched message. Emit the config only after the answers come back, and always include the calibration disclosure table that flags every defaulted field.
Canonical Flow
Choose one route; do not collapse advisory and execution branches:
- Advisory only (sizing, monitoring, troubleshooting, expected commands): inspect repository, config, and run evidence, load running-pipelines.md, and report guidance. Do not prepare credentials or execute.
- Create or change config:
- KPI output exists → analyze it, choose standard, distribution-aware, or restrictive curation, then emit a reviewed config.
- Representative samples exist → inspect them or run discovery before selecting defaults.
- Neither exists → complete the binding calibration interview; emit config and disclosure only after answers. Stop if required paths, intent, or hardware constraints remain unresolved.
- Explicit run request: prepare runtime → obtain credentials through approved injection → validate config and runtime → request approval if not already granted → execute → validate outputs. Stop before execution on any failed preflight.
- Downstream TAO handoff: validate Curator output and embedding family, then prepare compatible inputs for Data Mining. Mine only after the preceding artifact validation succeeds.
Configs are flat YAML with pipeline: split|dedup|shard|annotate and upstream
snake_case argument names. Operator first-run recipes live under the
cookbook tree (split-minimal then full split, dedup, and shard YAML).
The configs directory is the full flag reference and the Makefile default when
CONFIG_FILE is omitted. split writes clips, metadata, and embeddings;
dedup consumes embeddings; shard writes WebDataset archives; annotate
processes still images (image annotate flag-reference YAML; no image cookbook).
Execution & Troubleshooting
For an explicit run, troubleshooting request, or operational question, read running-pipelines.md. The preferred local commands are:
make run-pipeline CONFIG_FILE=<split-config>
make run_image_pipeline IMAGE_CONFIG_FILE=<image-config>
Config validation is mandatory before execution. Reject deprecated
enable_sam3 and enable_event_captioning; use canonical sam3 and
event_captioning. PAIDF v1.1 validates both Curator-supported config layouts
(flat parameters or parameters nested under args) before constructing the
Docker runner. Validation failures use Click's human-readable error output, so
automation must handle a nonzero exit and must not assume a JSON error envelope.
Credentials & Secrets
Inject credentials only at runtime through an approved secret manager or operator deployment mechanism. Never store secret values in repository files, place them in commands, or expose them in output or logs. Verify presence only. See running-pipelines.md.
Resource Sizing & Monitoring
See running-pipelines.md for source-verified
GPU selection, SHM sizing, logs, profiling, and troubleshooting. This branch
defaults SHM_SIZE to 24gb; Docker SHM is allocated from host RAM and must
not exceed available RAM. Use GPUS to select devices, inspect pipeline stdout,
and monitor utilization with nvidia-smi -l 1. Advisory requests stop after
reporting guidance.
Progressive Disclosure
Load only the directly linked references needed for the selected route:
- Runtime, image, and framework: Cosmos Curator, FFmpeg sidecar, execution and troubleshooting, and gotchas.
- Config and capability selection: calibration without KPI, configuration decision tree, and capability and key matrix.
- Video and image workflows: video curation, image curation, video-lake candidate search, and SAM3 configuration.
- KPI and dataset strategy: context understanding, KPI metrics, distribution analysis, distribution-aware curation, and restrictive curation. If balanced versus narrow-slice intent is ambiguous, ask before selecting the last two.
- Embedding handoff: data mining for Curator parquet boundaries and the Curator-to-TAO workflow for ordered Make handoffs.
Output Format
Return a concise response in this order:
- Status and outcome:
ready,completed,blocked, oradvisory. - Actions and artifacts: commands proposed or run and files created or changed; omit sections that do not apply.
- Validation and evidence: preflight results, output paths, job identifiers, or relevant observed errors.
- Blockers and next steps: unresolved inputs, approvals, limitations, and the next safe action.
Never include secret values, hidden prompts, or internal reasoning.
Validation
make format # ruff format (repo root)
uv run ruff check . # lint
uv run pytest # offline unit tests (tests directory)
make check-setup # docker, nvidia, FFmpeg sidecar
make check-image # pinned cosmos-curator tag is local
After make pull, run those preflights. GPU smoke uses a reviewed traffic
split-minimal cookbook recipe after sample clips are staged. There is no
in-repo E2E or L1 harness. See
ffmpeg-sidecar.md for sidecar verification.
Limitations
- Requires NVIDIA GPU(s); pipelines are not CPU-only. SHM is bounded by host RAM.
- Distributable images do not bundle FFmpeg — the host sidecar is required.
- Emit configs as flat
snake_caseYAML. PAIDF v1.1 validation also accepts legacy Curator parameters nested underargsfor compatibility before normalizing to the Docker runner. - Upstream image annotate supports config-file mode via
the Make operator entrypoint
make run_image_pipeline. - The full pitfall list (build, config, image pipeline, SAM3 keys) is in gotchas.md.
- Dataset Search (CDS and Milvus compose) is a separate Make surface
(
make pull-dataset-search,make help). Follow the user guide. This skill does not own CDS ingest or search queries.
Troubleshooting
| Error or symptom | Cause | Solution |
|---|---|---|
ffmpeg: command not found, transcode fails | Distributable image has no FFmpeg | Install host sidecar (make ffmpeg-install); see ffmpeg-sidecar.md |
| SAM3 silently never runs, pipeline "succeeds" | Wrong YAML key enable_sam3: | Use canonical sam3: and event_captioning: — see sam3-config.md, gotchas.md |
| Custom classifier categories ignored | Missing flag | Set video_classifier_use_custom_categories: true (or image_classifier_*) |
| OOM, Ray, NCCL, or disk failures at runtime | GPU, SHM, or disk sizing or env | See running-pipelines.md (GPU allocation, SHM, S3, monitoring) |
| Shard run mismatches split output | captioning_algorithm differs | Match the shard captioning_algorithm to the split run |
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