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SkillAtlasSkill 详情
tao-generate-image-grounding
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.
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。
- 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：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/tao-generate-image-grounding" 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/tao-generate-image-grounding" 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/tao-generate-image-grounding" 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/tao-generate-image-grounding" 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/tao-generate-image-grounding" 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: tao-generate-image-grounding
description: "Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them
to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded
annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding',
'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'."
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
metadata:
author: NVIDIA Corporation
version: "0.1.0"
allowed-tools: Read Bash Write
tags:
- image
- grounding
- bounding-boxes
- auto-label
- vlm
- 2d-groundingImage Grounding Pipeline
Turn (image, caption) pairs into per-image grounded annotations: cleaned captions, referring expressions with character spans, and pixel-space bounding boxes for each expression. A single VLM (Gemini or any OpenAI-compatible endpoint) handles both steps.
Purpose
Generate phrase-grounded training data for referring-expression and grounding models. The VLM acts as a "teacher" annotator: Step 0 extracts referring expressions from the caption while looking at the image; Step 1 returns one bbox set per expression for each image.
Pipeline Architecture
Step 0: Expression extraction → VLM cleans caption, extracts referring expressions + char spans
Step 1: Phrase grounding → VLM returns pixel bboxes + scores per expression
Steps are individually selectable via workflow.steps. Each step writes a per-sample checkpoint to step_<N>_*/.ckpt/<sample_id>.json and skips already-processed records on re-run. Set workflow.force_reprocess: true to ignore checkpoints and reprocess from scratch.
Instructions
Initial setup
When a user wants to run this pipeline, walk through these steps:
-
Input JSONL: Ask for the JSONL path. Each line must be one object like
{"image_path": "...", "caption": "..."}.image_pathcan be absolute or relative. -
Image root: If any
image_pathvalues are relative, setdata.image_rootto the directory they should resolve from. -
API access: Ask the user which VLM endpoint they want to use. Present these five options and act on the choice:
- Gemini — set
vlm.backend: "gemini"; requireGOOGLE_API_KEY(env var orvlm.gemini.api_key). - NIM (e.g.
https://inference-api.nvidia.com/v1) — setvlm.backend: "openai"; collectbase_url,model_name, andapi_key. - TAO inference microservice (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
- Running — collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai". - Not running — guide the user through the
skills/applications/tao-run-inference-serviceskill, which stands up a local TAO inference microservice with an OpenAI-compatible API. Before promising a specific model, checkskills/applications/tao-run-inference-service/references/service.yamlforvalid_network_arch_config_basenames. Once the server is up, collectbase_url,model_name, and (optionally)api_key; setvlm.backend: "openai".
- Running — collect
- vLLM (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
- Running — collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai". - Not running — follow references/vllm_server.md to install and launch a vLLM server, then collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai".
- Running — collect
- Custom (any other OpenAI-compatible endpoint) — set
vlm.backend: "openai"; collectbase_url,model_name, and (optionally)api_key.
If the user has no endpoint and does not want to set one up, stop and help resolve API access first.
- Gemini — set
-
Workflow steps: Choose one of:
- Full pipeline:
["0", "1"] - Expression extraction only:
["0"] - Grounding only:
["1"], which requires existing step-0 output atresults_dir/step_0_expression_extraction/annotations.jsonl
- Full pipeline:
-
Resume vs fresh run: By default, the workflow reuses checkpoints and skips completed records. To reprocess everything, set
image_grounding.workflow.force_reprocess=true.
Running the pipeline
The pipeline runs inside the TAO Toolkit container via the auto_label CLI:
auto_label generate -e /path/to/spec.yaml \
results_dir=/results \
image_grounding.data.input_jsonl=/data/captions.jsonl \
image_grounding.data.image_root=/data/images \
image_grounding.vlm.gemini.api_key=$GOOGLE_API_KEY
Generate a default spec: auto_label default_specs results_dir=/results module_name=auto_label, then set autolabel_type: "image_grounding". All fields support Hydra dot-notation overrides on the command line.
See references/configuration.md for the full YAML structure, all parameters, model/endpoint setup, and error patterns.
Recommended pilot workflow
- Run on 5-10 images with both steps
- Inspect
step_0_expression_extraction/annotations.jsonl— arecleaned_captionandexpressions[]accurate? Are the right noun phrases captured? - Inspect
step_1_grounding/annotations.jsonl— do the bboxes inexpressions[].instances[]look right? Are confidence scores reasonable? - If quality is insufficient, switch the VLM to a stronger model (e.g.
gemini-2.5-pro) or raisemedia_resolution/max_output_tokens, then re-run withforce_reprocess=true. - Scale to the full dataset once satisfied.
Configuration
Key configuration fields (full reference in references/configuration.md):
| Field | Default | Description |
|---|---|---|
workflow.steps | ["0","1"] | Which pipeline steps to execute ("0" = expressions, "1" = grounding) |
workflow.max_workers | 4 | Parallel threads per step (watch API rate limits) |
workflow.force_reprocess | false | Ignore per-sample checkpoints and reprocess from scratch |
vlm.backend | "gemini" | "gemini" or "openai" (OpenAI-compatible endpoint) |
data.input_jsonl | required | Path to input JSONL with image_path + caption per line |
data.image_root | "" | Optional prefix for resolving relative image_path entries |
Inputs
A single JSONL file at data.input_jsonl. One JSON object per line:
| Field | Required | Description |
|---|---|---|
image_path | yes | Absolute path, or relative path resolved against data.image_root |
caption | yes | Free-text caption for the image |
image_id | no | Stable identifier; auto-derived from the filename if missing |
width, height | no | Image dimensions in pixels; default to 1920×1080 for bbox clamping if missing |
Outputs
All outputs go to results_dir/:
step_0_expression_extraction/annotations.jsonl— per-record output enriched withcleaned_captionandexpressions[](each withtext,expression_id,char_span,noun_chunk, emptyinstances[]).step_1_grounding/annotations.jsonl— same records withexpressions[].instances[]filled in (each instance hasbbox: [x1,y1,x2,y2]in pixel space,scorein[0.0, 1.0], andbbox_id).results_dir/annotations.jsonl— copy of the last step's output for convenience.step_<N>_*/.ckpt/<sample_id>.json— per-sample checkpoints used for resume.
Prerequisites
- Container:
nvcr.io/nvidia/tao/tao-toolkit:6.26.3-pyt - API access: At least one VLM endpoint (Gemini API key or OpenAI-compatible endpoint capable of image input)
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