Cosmos3 Env Troubleshoot
NVIDIA/cosmos-framework
Diagnose and fix Cosmos3 environment, installation, and runtime errors.
Install Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills.
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills holoscan-install-container --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/holoscan-install-container .claude/skills/holoscan-install-container && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .claude/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-containerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills holoscan-install-container --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/holoscan-install-container .agents/skills/holoscan-install-container && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .agents/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills holoscan-install-container --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/holoscan-install-container .cursor/skills/holoscan-install-container && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .cursor/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/holoscan-install-container--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills holoscan-install-container --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/holoscan-install-container .gemini/skills/holoscan-install-container && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .gemini/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills holoscan-install-containerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/holoscan-install-container .github/skills/holoscan-install-container && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .github/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills holoscan-install-container --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/holoscan-install-container .opencode/skills/holoscan-install-container && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "holoscan-install-container" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-container into .opencode/skills/holoscan-install-container/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "holoscan-install-container", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
holoscan-install-containerInstall Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills.
Holoscan Install Container is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Install Holoscan SDK via the NGC Docker container. Use for container-based installs; not for native apt/pip/Conda installs.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).
It sits in DevOps & Cloud, covering Containers. It works with NVIDIA AI Platform, Docker, CUDA and Python. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
dockerpython3bashFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.nvidia.comdocs.docker.comcatalog.ngc.nvidia.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Holoscan Install Container loads about 1.9k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 492 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 492 words, ~1,861 tokens.
.claude/skills/holoscan-install-container/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Pull and verify the official Holoscan SDK container from NGC (nvcr.io/nvidia/clara-holoscan/holoscan), selecting the right CUDA/arch tag for the host GPU and validating with the bundled Python and C++ examples.
nvidia-smi).docker group (or sudo).docker run --gpus all works).nvcr.io and docs.nvidia.com.nvcr.io/nvidia/clara-holoscan/holoscan.Tag = <version>-<suffix>, e.g. v4.1.0-cuda13. Get the current SDK version from the doc page above; pick the suffix from nvidia-smi (the "CUDA Version" field, top-right of the table header):
nvidia-smi CUDA Version | Suffix |
|---|---|
| 13.x+ | cuda13 |
| 12.x, Ampere/Ada dGPU | cuda12-dgpu |
| 12.x, ARM64 iGPU (nvgpu) | cuda12-igpu |
The "CUDA Forward Compatibility mode ENABLED" banner is expected — not an error — when the container ships a newer CUDA minor version than the host driver supports. The forward-compat shim lets the container's CUDA runtime work against the older host driver within the same major version.
docker run --rm --gpus all ubuntu:22.04 nvidia-smi 2>&1 | tail -5If Docker is missing → install from https://docs.docker.com/engine/install/. If GPU passthrough fails → install the NVIDIA Container Toolkit per https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html, then retry.
Pull (~10–20 GB — warn the user before starting):
docker pull nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>Tests cover: bare Python binding (1a), bare C++ runtime (1b, 2a), Python + Holoviz/Vulkan (2b, 3a), and C++ + Holoviz/Vulkan (3b). Holoviz examples always run headless (inject headless: true into the YAML) — this works whether or not a display is attached and avoids GUI failure modes over SSH.
IMG=nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>
RUN=(docker run --rm --runtime=nvidia --gpus all --cap-add CAP_SYS_PTRACE --ipc=host --ulimit memlock=-1 --ulimit stack=67108864)
# 1a. hello_world (Python) — expect "Hello World!"
"${RUN[@]}" "$IMG" bash -c \
"ulimit -s 32768 && python3 /opt/nvidia/holoscan/examples/hello_world/python/hello_world.py"
# 1b. hello_world (C++) — expect "Hello World!"
"${RUN[@]}" "$IMG" bash -c \
"ulimit -s 32768 && /opt/nvidia/holoscan/examples/hello_world/cpp/hello_world"
# 2a. tensor_interop (C++) — expect tensors doubling each pass, "Graph execution finished."
"${RUN[@]}" "$IMG" bash -c \
"ulimit -s 32768 && /opt/nvidia/holoscan/examples/tensor_interop/cpp/tensor_interop"
# 2b. tensor_interop (Python, 10 frames) — Holoviz, headless. The YAML has no
# headless field by default, so inject one under `holoviz:`. Expect
# "message received (count: 10)".
"${RUN[@]}" "$IMG" bash -c "
ulimit -s 32768
sed -e 's/count: 0/count: 10/' \
-e 's/repeat: true/repeat: false/' \
-e 's/realtime: true/realtime: false/' \
-e 's/^holoviz:/holoviz:\n headless: true/' \
/opt/nvidia/holoscan/examples/tensor_interop/python/tensor_interop.yaml > /tmp/ti.yaml
cd /opt/nvidia/holoscan/examples/tensor_interop/python
python3 tensor_interop.py --config /tmp/ti.yaml
"
# 3a. video_replayer (Python, 10 frames) — Holoviz, headless. Inject `headless: true`
# under `holoviz:` (above `width: 854`). Same sed works for the C++ YAML in 3b —
# both files share the same `holoviz:` section shape.
"${RUN[@]}" "$IMG" bash -c "
ulimit -s 32768
sed -e 's/count: 0/count: 10/' \
-e 's/repeat: true/repeat: false/' \
-e 's/realtime: true/realtime: false/' \
-e 's/^ width: 854/ headless: true\n width: 854/' \
/opt/nvidia/holoscan/examples/video_replayer/python/video_replayer.yaml > /tmp/vr.yaml
cd /opt/nvidia/holoscan/examples/video_replayer/python
HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data python3 video_replayer.py --config /tmp/vr.yaml
"
# 3b. video_replayer (C++, 10 frames) — same headless injection as 3a. The C++
# YAML hard-codes `directory: "../data/racerx"`, but HOLOSCAN_INPUT_PATH
# overrides it, so we don't need to patch that field.
"${RUN[@]}" "$IMG" bash -c "
ulimit -s 32768
sed -e 's/count: 0/count: 10/' \
-e 's/repeat: true/repeat: false/' \
-e 's/realtime: true/realtime: false/' \
-e 's/^ width: 854/ headless: true\n width: 854/' \
/opt/nvidia/holoscan/examples/video_replayer/cpp/video_replayer.yaml > /tmp/vr_cpp.yaml
cd /opt/nvidia/holoscan/examples/video_replayer/cpp
HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data ./video_replayer --config /tmp/vr_cpp.yaml
"docker run -it --rm \
--runtime=nvidia --gpus all --cap-add CAP_SYS_PTRACE \
--ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>
# Examples: /opt/nvidia/holoscan/examples/
# Mount files: -v /host/path:/container/path
# GUI examples: add -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAYNext:
ls /opt/nvidia/holoscan/examples//holoscan-explain-exampledocker: Error response from daemon: could not select device driver "nvidia". NVIDIA Container Toolkit is missing or not configured. Install per the link in Step 2 and restart Docker.nvidia-smi CUDA Version and the table in Step 1.ulimit -s 32768 wasn't applied inside the container. Use the bash -c "ulimit -s 32768 && ..." pattern shown in Step 3.headless: true. Use the sed injection shown in Step 3.video_replayer can't find data. Set HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data — overrides the YAML's hard-coded path.© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files in skills/holoscan-install-container of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Holoscan Install Container next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Holoscan Install Container this skillNVIDIA/skills | 3.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Cosmos3 Env TroubleshootNVIDIA/cosmos-framework | 558 | — | ~1.3k | Automated safety check: Notes | Custom licence | |
| Generate Nemo Gym Envadithya-s-k/FineEnvs | 443 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Init GPU Serverdrawthingsai/draw-things-community | 580 | — | ~2.2k | Automated safety check: Pass | GPL-3.0 | |
| Migrate Workflow Ec2 To Osdcpytorch/test-infra | 113 | — | ~2k | Automated safety check: Pass | Custom licence | |
| Vllm Deploy Dockervllm-project/vllm-skills | 103 | — | ~2.5k | Automated safety check: Notes | Apache-2.0 |
NVIDIA/cosmos-framework
Diagnose and fix Cosmos3 environment, installation, and runtime errors.
adithya-s-k/FineEnvs
Builds a NeMo Gym (NVIDIA) variant of an RL environment. An agent skill from adithya-s-k/FineEnvs.
drawthingsai/draw-things-community
Initialize a Draw Things GPU server with GPUScript, including script sync, Docker/CUDA/NVIDIA runtime setup, 7T data disk mounting, mergerfs, and end-to-end GPU verification.
pytorch/test-infra
Step-by-step playbook for migrating a pytorch/pytorch .github/workflows/.yml from EC2 to OSDC (ARC) runners — covers both dial-up and 100% opt-in patterns, with the inputs that must be plumbed…
vllm-project/vllm-skills
Deploy vLLM using Docker (pre-built images or build-from-source) with NVIDIA GPU support and run the OpenAI-compatible server.
VectorSpaceLab/AREX-Skill
Use this repo skill for DiscoArt image generation, configuration/prompt scheduling, CLI, Jina serving, Docker runtime planning, and troubleshooting.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Works with
Categories
Install Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills. Holoscan Install Container is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Install Holoscan SDK via the NGC Docker container.
Holoscan Install Container fits situations like: container-based installs; not for native apt/pip/Conda installs.
Run `npx skills add NVIDIA/skills --skill holoscan-install-container -a claude-code`. Or copy the skill folder (skills/holoscan-install-container in NVIDIA/skills) into .claude/skills/holoscan-install-container in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill holoscan-install-container -a codex`. Or copy the skill folder (skills/holoscan-install-container in NVIDIA/skills) into .agents/skills/holoscan-install-container in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill holoscan-install-container -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/holoscan-install-container, .gemini/skills/holoscan-install-container, .github/skills/holoscan-install-container and .opencode/skills/holoscan-install-container in your project.
Going by SKILL.md and its folder, Holoscan Install Container needs the command-line tools its instructions call (docker, python3 and bash). Our summary lists: Python 3; Docker.
SKILL.md names 3 domains. As links in the text: docs.nvidia.com, docs.docker.com and catalog.ngc.nvidia.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Holoscan Install Container is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Holoscan Install Container: Cosmos3 Env Troubleshoot (NVIDIA/cosmos-framework, 558 stars), Generate Nemo Gym Env (adithya-s-k/FineEnvs, 443 stars), Init GPU Server (drawthingsai/draw-things-community, 580 stars) and Migrate Workflow Ec2 To Osdc (pytorch/test-infra, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.