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
deepstream-dev
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-*, cufolio, 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
│ ├── 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.
开发与工程数据与 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。
- 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：2 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/example/repo.git
- 将 "repo" 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/example/repo.git
- 将 "repo" 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/example/repo.git
- 将 "repo" 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/example/repo.git
- 将 "repo" 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/example/repo.git
- 将 "repo" 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: deepstream-dev
description: NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
owner: NVIDIA CORPORATION
service: deepstream
version: 1.1.1
reviewed: 2026-04-24
license: CC-BY-4.0 AND Apache-2.0DeepStream Development Skill
When this skill is active, ALWAYS read the relevant reference documents before generating code. Do NOT rely on memory - the reference documents contain critical details about exact property names, correct API usage, and common pitfalls.
SDK and Architecture Quick Reference
DeepStream SDK Version Requirements
- GStreamer: 1.24.2
- NVIDIA Driver: 590+
- CUDA: 13.1
- TensorRT: 10.14.1.48
- Platforms: Ubuntu 24.04 (x86_64 and ARM64/Jetson)
Typical Pipeline Flow
Source → Stream Muxer → Inference → [Tracker] → OSD → Renderer
Components in [brackets] are optional -- only add them when the user explicitly requests them.
| Stage | Role | Key Element(s) | Required? |
|---|---|---|---|
| Source | Input from files, RTSP, cameras | nvurisrcbin (preferred), nvmultiurisrcbin, filesrc | Yes |
| Stream Muxer | Batches streams for inference | nvstreammux | Yes |
| Inference | TensorRT model execution | nvinfer, nvinferserver | Yes |
| Tracker | Multi-object tracking across frames | nvtracker | Only if requested |
| OSD | Draws bounding boxes, labels, overlays | nvosdbin | Yes (for visualization) |
| Renderer | Display or save output | nveglglessink, nv3dsink, filesink | Yes |
Memory Model
DeepStream uses NVIDIA Video Memory Manager (NVMM) for zero-copy GPU buffer transfers. Caps strings use memory:NVMM to indicate GPU memory (e.g., video/x-raw(memory:NVMM), format=NV12).
Critical Rules
-
Only Add Requested Components: Do NOT add pipeline elements the user did not ask for.
- Tracker (
nvtracker): Only add when the user explicitly requests tracking or object IDs across frames - Secondary GIEs: Only add when the user requests classification or attribute extraction
- Analytics (
nvdsanalytics): Only add when the user requests line crossing, ROI counting, etc. - Message broker (
nvmsgbroker/nvmsgconv): Only add when the user requests Kafka/cloud messaging - When in doubt, build the minimal working pipeline and let the user ask for additions
- Tracker (
-
Default to
nvurisrcbinfor Sources: When the user says "camera", "stream", "video", or provides a file path:- Always use
nvurisrcbin-- it handles RTSP, HTTP, and local files (file://) transparently - Only use
filesrc+qtdemux+ parser when the user explicitly needs raw file source control - For RTSP/live sources, also set
live-source=1onnvstreammuxandsync=0on the sink - Convert local paths to URI:
"file://" + os.path.abspath(path)
- Always use
-
Metadata Iteration: Use
.frame_itemsand.object_items(returns iterators, NOT lists)- NEVER use
len()on these - iterate to count - Iterator can only be consumed once
- NEVER use
-
Request Pad Syntax: Use
"sink_%u"template, NEVER literal pad namespipeline.link(("decoder", "mux"), ("", "sink_%u")) # CORRECT # pipeline.link(("decoder", "mux"), ("", "sink_0")) # WRONG - will fail -
Platform Detection for Sinks:
import platform sink_type = "nv3dsink" if platform.processor() == "aarch64" else "nveglglessink" -
Buffer Cloning: Always clone buffers for async processing
tensor = buffer.extract(0).clone() # CRITICAL -
Queue Types:
queue.Queue→ Use withthreading.Threadmultiprocessing.Queue→ Use withmultiprocessing.Process- Using wrong type causes silent data loss!
-
nvinfer Config Format:
- YAML: Use
property:section (NOTmodel:),key: valuewith space after colon - INI: Use
[property]section,key=valuewith equals sign - Section MUST be named
property
- YAML: Use
-
nvmsgbroker is a SINK: Cannot have downstream elements - use
teeto split pipeline -
ALL Sinks Need async=0 for Tee Splits or Dynamic Sources: CRITICAL for state transitions
# When using tee splits OR dynamic sources, ALL sinks MUST have async=0 pipeline.add("nveglglessink", "sink", { "sync": 0, "qos": 0, "async": 0 # CRITICAL - prevents state transition deadlock })Symptom if missing: Pipeline stays in PAUSED state, no video displays.
-
Built-in Probe Attachment:
measure_fps_probecan only be attached to processing elements (e.g.,nvinfer,nvosdbin), NOT to sink elements. Attaching to a sink raisesRuntimeError: Probe failure. -
Dynamic ONNX Models Require
infer-dims: When the ONNX model has dynamic input shapes (e.g., exported withdynamic=Truein Ultralytics YOLO, or with dynamic batch/height/width axes), you MUST addinfer-dims=C;H;Wto the nvinfer config. Without it, TensorRT sees-1for dynamic dimensions and fails withsetDimensions: Error Code 3. Common values:- YOLO models (640 input):
infer-dims=3;640;640 - Models with 416 input:
infer-dims=3;416;416 - Models with 1280 input:
infer-dims=3;1280;1280
- YOLO models (640 input):
-
Ultralytics YOLO Output Format Depends on Model Generation — newer models (v10+/v26+) output post-NMS results; older models (v8/v11) output raw pre-NMS tensors. The custom parser and
cluster-modemust match the actual output:
| Model generation | Output tensor shape | Fields | cluster-mode |
|---|---|---|---|
| v8 / v11 | [batch, 84, 8400] | [features(4+80), anchors] — raw cx/cy/w/h + class scores, no NMS | 2 (NMS) |
| v10 / v26+ | [batch, 300, 6] | [max_det, (x1,y1,x2,y2,conf,cls)] — already post-NMS, pixel coords | 4 (none) |
How to identify at runtime: log inferDims.d[0] and inferDims.d[1] inside the custom parser.
d={84, 8400}→ pre-NMS (v8/v11 style)d={300, 6}→ post-NMS (v10/v26+ style)
Symptom of mismatch: If cluster-mode: 2 is used with a post-NMS [N, 6] output, bounding boxes appear shifted by 45° or 135° from the actual objects (DeepStream's NMS incorrectly re-processes already-final coordinates).
If you see tilted or rotated boxes, also check the OBB / rotation_angle note in references/nvinfer_config.md: for non-OBB models, value-initialize NvDsInferObjectDetectionInfo with obj{} and keep rotation_angle = 0; plain NvDsInferObjectDetectionInfo obj; leaves fields uninitialized.
- Virtual Environment Must Include pyservicemaker:
pyservicemakeris installed system-wide but is NOT accessible from a standard Python virtual environment. When a task requires a venv (e.g., for model download/conversion pip dependencies), always installpyservicemakerandpyyamlinside the venv. The venv setup in generated code and README must always include:
Symptom if missing:python3 -m venv venv source venv/bin/activate pip install /opt/nvidia/deepstream/deepstream/service-maker/python/pyservicemaker*.whl pyyaml pip install -r requirements.txt # other dependenciesModuleNotFoundError: No module named 'pyservicemaker'when running the app inside the venv.
Key Paths
- Models:
/opt/nvidia/deepstream/deepstream/samples/models/ - Primary Detector:
/opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet18_trafficcamnet_pruned.onnx - Tracker lib:
/opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so - Kafka lib:
/opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so - Sample configs:
/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/
Reference Documents
IMPORTANT: Always read these documents for complete details. Do NOT generate code from memory.
| Document | Use When |
|---|---|
| references/gstreamer_plugins.md | Looking up plugin properties, ALL properties listed |
| references/service_maker_api.md | Using Pipeline/Flow API, metadata access, probes, EventMessageUserMetadata |
| references/use_cases_pipelines.md | Building pipelines: simple playback, multi-inference, cascaded GIE |
| references/streaming_sources.md | Ingesting local files, HTTP MP4, HLS, MPEG-DASH, or RTSP sources with nvurisrcbin |
| references/kafka_messaging.md | Kafka/message broker setup, nvmsgconv/nvmsgbroker config, msg2p-newapi |
| references/best_practices.md | Design patterns, common pitfalls, anti-patterns |
| references/buffer_apis.md | BufferProvider/Feeder (injection), BufferRetriever/Receiver (extraction) |
| references/media_extractor_advanced.md | MediaExtractor, MediaChunk, FrameSampler |
| references/utilities_config.md | PerfMonitor, EngineFileMonitor, SourceConfig, SensorInfo, SmartRecordConfig |
| references/nvinfer_config.md | nvinfer config file format, ALL parameters |
| references/tracker_config.md | nvtracker config, NvDCF/IOU/DeepSORT/NvSORT |
| references/troubleshooting.md | Error messages and solutions |
| references/rest_api_dynamic.md | REST API, dynamic source add/remove, nvmultiurisrcbin |
| references/metamux_config.md | nvdsmetamux config, parallel multi-model inference, metadata merging, source ID filtering |
| references/docker_containers.md | Docker images, Dockerfile examples, pyservicemaker install, container run commands |
| references/nvds_msgapi_adapter.md | Building custom protocol adapters: nvds_msgapi |
Quick Error Reference
| Error | Solution |
|---|---|
iterator has no len() | Iterate to count, don't use len() |
pad template not found | Use "sink_%u" not "sink_0" |
| Queue data loss | Use multiprocessing.Queue with Process |
| Config parse failed | Use property: not model: in YAML |
is-classifier deprecation warning | Use network-type: 1 instead of is-classifier: 1 for classifiers; omit both for detectors |
min-boxes unknown key warning | Use minBoxes (camelCase) in class-attrs-* sections, not min-boxes |
| Secondary GIE inactive | Set process-mode: 2, check operate-on-gie-id |
| Tee/dynamic source stuck PAUSED | Set async: 0 on ALL sink elements |
| RTSP no data/reconnecting | Test URL with ffplay, check credentials |
RuntimeError: Probe failure | measure_fps_probe cannot attach to sink elements; use nvinfer or nvosdbin instead |
setDimensions negative dims / engine build failed | Add infer-dims=C;H;W for dynamic ONNX models (e.g., infer-dims=3;640;640) |
No module named 'pyservicemaker' in venv | pip install /opt/nvidia/deepstream/deepstream/service-maker/python/pyservicemaker*.whl pyyaml inside the venv |
AttributeError: object has no attribute 'obj_label' | Use obj_meta.label not obj_meta.obj_label in pyservicemaker (C API name differs from Python binding) |
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