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SkillAtlasSkill 详情
rag-blueprint
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.
数据与 AI测试与质量DevOps 与部署研究与检索
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。
- 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：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/NVIDIA/skills.git
- 将 "skills/rag-blueprint" 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/rag-blueprint" 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/rag-blueprint" 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/rag-blueprint" 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/rag-blueprint" 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: rag-blueprint
version: "2.6.0"
description: "NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more)."
license: Apache-2.0
compatibility: >-
NVIDIA RAG Blueprint repository checkout; Docker/Compose or Kubernetes/Helm
for deployments; Python 3.11+ for library workflows; NVIDIA GPU tooling for
self-hosted NIM services.
metadata:
author: "NVIDIA RAG <foundational-rag-dev@exchange.nvidia.com>"
github-url: "https://github.com/NVIDIA-AI-Blueprints/rag"
endpoint-openapi-schemas:
- docs/api_reference/openapi_schema_rag_server.json
- docs/api_reference/openapi_schema_ingestor_server.json
argument-hint: deploy RAG | enable feature | disable feature | configure | troubleshoot | shutdown
tags:
- nvidia
- blueprint
- rag
- deployment
- configuration
- troubleshooting
languages:
- python
- typescript
- shell
frameworks:
- fastapi
- langchain
- react
- docker-compose
- helm
domain: ai-ml
allowed-tools: Bash(echo *) Bash(nvidia-smi *) Bash(curl --version *) Bash(docker ps *) Bash(docker info *) Bash(docker --version *) Bash(docker version *) Bash(docker logs *) Bash(docker inspect *) Bash(docker stats *) Bash(docker compose ps *) Bash(docker compose logs *) Bash(docker compose config *) Bash(docker compose version *) Bash(kubectl get *) Bash(kubectl describe *) Bash(kubectl version *) Bash(kubectl logs *) Bash(kubectl api-resources *) Bash(kubectl rollout status *) Bash(helm version *) Bash(helm list *) Bash(helm status *) Bash(oc get *) Bash(oc describe *) Bash(oc logs *) Bash(oc whoami *) Bash(oc version *) Bash(git rev-parse *) Bash(git describe *) Bash(git status *) Bash(python3 --version *) Bash(pip3 show *) Bash(df *) Bash(du *) Bash(cat /proc/*) Bash(cat /etc/os-release *) Bash(ss *) Bash(netstat *) Bash(ls *) Bash(grep *) Bash(lsof *) Bash(ps aux *) Read Grep GlobNVIDIA RAG Blueprint
Purpose
Use this skill for NVIDIA RAG Blueprint operations: deployment, configuration, troubleshooting, shutdown, and feature management across Docker, Helm, and library deployments.
Instructions
- Match the user request to the intent routing table below.
- Read the referenced playbook before making changes.
- Use repository docs and deployment config files as the source of truth.
- Verify the affected service or workflow after changes.
Prerequisites
- NVIDIA RAG Blueprint repository checkout.
- Docker/Compose or Kubernetes/Helm for deployments.
- Python 3.11+ for library workflows.
- NVIDIA GPU tooling for self-hosted NIM services.
Autonomy Principles
- Auto-detect everything: GPU, VRAM, drivers, Docker, CUDA, disk, OS, ports, existing services, NGC key, repo state.
- If it can be checked with a command, check it — don't ask the user.
- Ask only when user action is required: providing an API key, confirming data deletion, or choosing between equally valid options.
- Once analysis is done, route to the correct workflow and execute.
Intent Detection
Determine what the user wants and route immediately:
| User Intent | Action |
|---|---|
| Deploy, install, set up, start RAG | Read and follow references/deploy.md |
| Configure, enable, change, toggle a feature | Use the Configure section below |
| Troubleshoot, debug, fix, error, unhealthy | Read and follow references/troubleshoot.md |
| Stop, shutdown, tear down, clean up | Read and follow references/shutdown.md |
If the intent is ambiguous, infer from context (e.g., "RAG isn't working" → troubleshoot; "get RAG running" → deploy). Only ask if genuinely unclear.
Configure
Requires a running RAG deployment. If services are not running, deploy first via references/deploy.md.
Match the user's request to a reference file, then read and follow it:
| Feature Keywords | Reference |
|---|---|
| VLM, VLM embeddings, image captioning | references/configure/vlm.md |
| NeMo Guardrails | references/configure/guardrails.md |
| Agentic RAG, planning/execution agent, agentic streaming, stage events | references/configure/agentic-rag.md |
| Query rewriting, decomposition, multi-turn | references/configure/query-and-conversation.md |
| Ingestion (text-only, audio, Nemotron Parse, OCR, batch CLI, NV-Ingest, volume mount, performance) | references/configure/ingestion.md |
| Search, retrieval, hybrid search, multi-collection, metadata, filters, Elasticsearch filters, reranker, topK, accuracy/performance | references/configure/search-and-retrieval.md |
| LLM/embedding/ranking model changes, vector DB, Milvus/Elasticsearch auth, service keys, model profiles, ports/GPU | references/configure/models-and-infrastructure.md |
Reasoning, thinking mode, reasoning_content, self-reflection, prompts, generation params (tokens, temperature, citations), per-request LLM params | references/configure/reasoning-and-generation.md |
| Summarization | references/configure/summarization.md |
| Observability (tracing, Zipkin, Grafana, Prometheus) | references/configure/observability.md |
| Multimodal query (image + text) | references/configure/multimodal-query.md |
| Data catalog (collection/document metadata) | references/configure/data-catalog.md |
| User interface (UI settings, reasoning panel, metadata filters) | references/configure/user-interface.md |
| API reference (endpoints, schemas) | references/configure/api-reference.md |
| Evaluation (RAGAS metrics) | references/configure/evaluation.md (and skill rag-eval) |
| MCP server & client, agent toolkit | references/configure/mcp.md |
| Migration (version upgrades) | references/configure/migration.md |
| Notebooks (setup and catalog) | references/configure/notebooks.md |
Configure Flow
-
Match the user's request to a reference file from the table above.
-
Detect what's running:
echo "=== NIM ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(nim-llm|nemotron-(vlm-)?embedding|nemotron-ranking|nemotron-vlm|nemotron-3-nano-omni|page-elements|graphic-elements|table-structure|nemotron-ocr)' || echo "NO_LOCAL_NIMS"; echo "=== RAG ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(rag-server|ingestor-server|elasticsearch|milvus|seaweedfs|lancedb)' || echo "NO_DOCKER_RAG"; echo "=== K8S ===" && kubectl get pods -n rag 2>/dev/null | head -5 || echo "NO_K8S"; echo "=== LIBRARY ===" && ps aux 2>/dev/null | grep -E '(nvidia_rag|uvicorn.*rag)' | grep -v grep || echo "NO_LIBRARY" -
Use this table to determine platform, deployment type, and where config lives:
Local NIMs running? RAG services running? Deployment Type Config Location Yes (Docker) Any Self-hosted deploy/compose/.envNo Yes (Docker) NVIDIA-hosted deploy/compose/nvdev.envYes (K8s pods) Any Self-hosted values.yaml(NIM sections)No Yes (K8s pods) NVIDIA-hosted values.yaml(envVars)— Library processes Library mode notebooks/config.yamlNo No Not running Deploy first via references/deploy.mdTell the user what you detected and ask to confirm. Example: "I see local NIM containers running (nim-llm-ms, nemotron-vlm-embedding-ms) — this is a self-hosted deployment. Config file is
deploy/compose/.env. Correct?" -
Check current feature state before changing anything — read the config location from step 3, then cross-check the live service:
- Docker:
docker exec rag-server env 2>/dev/null | grep -E "<VAR_NAME>" - Helm:
kubectl get pod -n rag -l app=rag-server -o jsonpath='{.items[0].spec.containers[0].env}' 2>/dev/null
If the config file and live service disagree, tell the user the service has stale config and will need a restart.
- Docker:
-
If the feature needs extra GPUs, check availability against hardware restrictions (see below):
nvidia-smi --query-gpu=index,name,memory.total,memory.used --format=csv,noheader 2>/dev/null || echo "NO_GPU" -
Read the reference file and apply changes:
- Docker: edit the env file (uncomment to enable, re-comment to disable — the env file is the source of truth). Then restart the affected service:
source <env-file> && docker compose -f deploy/compose/<compose-file> up -dService Compose File rag-server docker-compose-rag-server.yamlingestor-server docker-compose-ingestor-server.yamlElasticsearch, Milvus, etcd, SeaweedFS vectordb.yamlNIM containers (LLM, embedding, ranking, VLM, OCR, parse, audio, extraction) nims.yamlguardrails docker-compose-nemo-guardrails.yamlobservability (Grafana, Prometheus, Zipkin) observability.yaml - Helm: edit
values.yaml, then upgrade:helm upgrade rag <chart> -n rag -f values.yaml - Library: edit
notebooks/config.yaml, then restart the Python process
- Docker: edit the env file (uncomment to enable, re-comment to disable — the env file is the source of truth). Then restart the affected service:
-
Verify:
- Docker:
docker ps --format "table {{.Names}}\t{{.Status}}" | head -20; curl -s http://localhost:8081/v1/health?check_dependencies=true 2>/dev/null | head -1 - Helm:
kubectl get pods -n rag; kubectl rollout status deployment/rag-server -n rag --timeout=120s - Library:
curl -s http://localhost:8081/v1/health 2>/dev/null | head -1
- Docker:
-
If restart fails, read
references/troubleshoot.md. If multiple features requested, repeat from step 1 for each.
Examples
- "Deploy RAG" -> route to
references/deploy.md. - "Enable VLM" -> route to
references/configure/vlm.md. - "RAG is unhealthy" -> route to
references/troubleshoot.md. - "Stop RAG" -> route to
references/shutdown.md.
Limitations
- Operational guidance only applies to this RAG Blueprint repository.
- Live deployment changes require a running Docker, Helm, or library target.
- Secrets such as
NGC_API_KEYmust be supplied by the user environment.
Troubleshooting
| Error / signal | What to do |
|---|---|
| Services are not running | Follow references/deploy.md before configuring features. |
| Restart or health check fails | Follow references/troubleshoot.md. |
| User requests teardown | Follow references/shutdown.md and confirm destructive cleanup. |
When User Says "Configure" Without Specifics
Run steps 2–3 above, then read the identified config file to list what's currently enabled:
grep -E "^(export )?(ENABLE_|APP_)" <config-file> 2>/dev/null | sort
Summarize what's running and enabled, then ask which feature to change.
Hardware Restrictions
Read docs/support-matrix.md for current GPU requirements per deployment mode.
Read docs/service-port-gpu-reference.md for port mappings and GPU assignments.
| GPU | Feature Restrictions |
|---|---|
| B200 | No VLM, No Guardrails, No Nemotron Parse. May need multi-GPU LLM (LLM_MS_GPU_ID). |
| RTX PRO 6000 | No Nemotron Parse. No Audio on Helm. |
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