LLM Torch Profiler Analysis
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
Clone the latest NVIDIA Holoscan Sensor Bridge repo, ask which supported devkit is being used, configure the host per platform, build the correct demo container, run it, and verify HSB connectivity…
$ npx skills add NVIDIA/skills --skill hsb-setup -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills hsb-setup --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/hsb-setup .claude/skills/hsb-setup && 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 "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .claude/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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/hsb-setupType 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 hsb-setup -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills hsb-setup --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/hsb-setup .agents/skills/hsb-setup && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .agents/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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 hsb-setup -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills hsb-setup --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/hsb-setup .cursor/skills/hsb-setup && 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 "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .cursor/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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/hsb-setup--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 hsb-setup -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills hsb-setup --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/hsb-setup .gemini/skills/hsb-setup && 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 "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .gemini/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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 hsb-setupInstalls 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 hsb-setup -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/hsb-setup .github/skills/hsb-setup && 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 "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .github/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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 hsb-setup -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 hsb-setup --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/hsb-setup .opencode/skills/hsb-setup && 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 "hsb-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/hsb-setup into .opencode/skills/hsb-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hsb-setup", 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.
hsb-setupClone the latest NVIDIA Holoscan Sensor Bridge repo, ask which supported devkit is being used, configure the host per platform, build the correct demo container, run it, and verify HSB connectivity…
Hsb Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Clone the latest NVIDIA Holoscan Sensor Bridge repo, ask which supported devkit is being used, configure the host per platform, build the correct demo container, run it, and verify HSB connectivity by pinging 192.168.0.2. Use for Holoscan Sensor Bridge setup, build, container launch, and first-connectivity bring-up.
Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts and reference files (for example `BENCHMARK.md`, `docs/failure-playbook.md` and `docs/platform-mapping.md`).
It works with NVIDIA AI Platform. 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.
12 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditMultiEditGrepGlobBashFrom allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Shell, PowerShell and Batch), which the agent can run.
Shell commands in SKILL.md call:
shFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
github.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.
Hsb Setup loads about 4.9k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 2,007 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 noted patterns worth knowing about, such as sudo or a known installer.
REMOTE_SUDO sudo / sudo -n / "" — default to "sudo" if not set.for privileged commands. Accept `sudo`, `sudo -n`, or empty stringallowed-tools: Read, Write, Edit, MultiEdit, Grep, Glob, BashAutomated 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); the scripts in this folder are not scanned.
The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 2,007 words, ~4,898 tokens.
.claude/skills/hsb-setup/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.Use this skill when the user wants to bring up the Holoscan Sensor Bridge demo environment end to end.
This workflow has side effects. Never run it automatically. Only run it when the user explicitly invokes it.
Gate 1 — Read environment variables. Before doing anything else, check these variables and print their resolved values to the user:
SSH_TARGET Remote devkit login (e.g. nvidia@192.168.1.50). Ask the user if not set.
REMOTE_ROOT Remote working directory (e.g. /home/nvidia). Ask the user if not set.
REMOTE_SUDO sudo / sudo -n / "" — default to "sudo" if not set.
REMOTE_SSH_OPTS Additional SSH options (optional).
HSB_PLATFORM Platform hint — may be empty; will detect from hardware.
HSB_REPO Custom repo URL — defaults to https://github.com/nvidia-holoscan/holoscan-sensor-bridge.gitSSH_TARGET and REMOTE_ROOT are required. Stop and ask the user for them if either is missing.
Gate 2 — Present the phase plan. Before taking any action, show the user this exact plan and wait for acknowledgement:
HSB Setup — Phase Plan
Phase 0: Token-budget preflight
Phase 1: Confirm platform, set up SSH, clone repo, study user guide
Phase 2: Host prerequisite checks and network setup
Phase 3: Native CLI build (AGX Thor only — skipped for other platforms)
Phase 4: Build demo container, run it, ping 192.168.0.2, verify FPGA version
Phase 5: Issues report (with option to save)
Phase 6: Stop apps, exit container, hand control back to userGate 3 — Token-budget preflight (Phase 0). Run this before any SSH connection or devkit change. See ## Token-budget preflight section for the full procedure. Do not proceed to Phase 1 until the budget check passes.
Invoke this skill by typing /hsb-setup [PLATFORM] [OPTIONS]. The skill walks through each phase interactively, prompting for confirmation before making changes.
cat /sys/class/dmi/id/product_name on the devkit and comparing the result to the HSB_PLATFORM environment variable using the product-name-to-platform mapping (see "Host platform auto-detection" section). If the command returns a recognized non-empty platform name that differs from HSB_PLATFORM, or if HSB_PLATFORM is empty, update HSB_PLATFORM to match the detected platform and alert the user about the change. If the command returns empty or fails and HSB_PLATFORM is already set, keep the existing value.main branch. By default this is the public nvidia-holoscan/holoscan-sensor-bridge repo, but the user can override it with a custom repo URL via the HSB_REPO environment variable or the --repo <URL> command-line flag. if the repo is an ssh repo, alert the user if no ssh key is set and provide instructions how to set up the ssh key.192.168.0.2. if the connection to the board fails, prompt the user for a possiblity of a different ip address.Use the following mapping unless the repository or current docs in the working tree clearly say otherwise:
sh docker/build.sh --dgpush docker/build.sh --igpush docker/build.sh --igpush docker/build.sh --igpush docker/build.sh --igpuIf the user says only “IGX Orin”, explicitly ask whether it is iGPU or dGPU OS/configuration.
During Phase 1 (after SSH is established or when running locally), verify the actual devkit hardware by reading the DMI product name and comparing it to the HSB_PLATFORM environment variable.
The following table maps known /sys/class/dmi/id/product_name values to supported HSB_PLATFORM values. Match using case-insensitive substring search — the product name may contain additional text (e.g., "Developer Kit", revision numbers).
product_name contains (case-insensitive) | Mapped HSB_PLATFORM | Notes |
|---|---|---|
IGX Orin | IGX Orin | Still need to ask iGPU vs dGPU if not already known |
AGX Orin | AGX Orin | |
AGX Thor | AGX Thor | |
DGX Spark | DGX Spark |
If the product name does not match any known pattern, treat it as unrecognized and fall through to the manual platform question in step 5.
Run the following on the devkit (inside the Phase 1 SSH heredoc or locally):
DETECTED_PRODUCT=""
if [ -f /sys/class/dmi/id/product_name ]; then
DETECTED_PRODUCT=$(cat /sys/class/dmi/id/product_name 2>/dev/null | tr -d '\n')
fi
DETECTED_PLATFORM=""
if echo "$DETECTED_PRODUCT" | grep -qi "IGX Orin"; then
DETECTED_PLATFORM="IGX Orin"
elif echo "$DETECTED_PRODUCT" | grep -qi "AGX Orin"; then
DETECTED_PLATFORM="AGX Orin"
elif echo "$DETECTED_PRODUCT" | grep -qi "AGX Thor"; then
DETECTED_PLATFORM="AGX Thor"
elif echo "$DETECTED_PRODUCT" | grep -qi "DGX Spark"; then
DETECTED_PLATFORM="DGX Spark"
fi
echo "DETECTED_PRODUCT=$DETECTED_PRODUCT"
echo "DETECTED_PLATFORM=$DETECTED_PLATFORM"
echo "HSB_PLATFORM=${HSB_PLATFORM:-}"After collecting the output, apply the following reconciliation rules:
DETECTED_PLATFORM is non-empty and HSB_PLATFORM is empty → set HSB_PLATFORM to DETECTED_PLATFORM. Alert the user:
Platform auto-detected from hardware: <DETECTED_PLATFORM> (product_name: <DETECTED_PRODUCT>).
HSB_PLATFORM was not set — updating to "<DETECTED_PLATFORM>".DETECTED_PLATFORM is non-empty and differs from HSB_PLATFORM → override HSB_PLATFORM with DETECTED_PLATFORM. Alert the user:
WARNING: Hardware reports "<DETECTED_PLATFORM>" (product_name: <DETECTED_PRODUCT>),
but HSB_PLATFORM was set to "<HSB_PLATFORM>".
Updating HSB_PLATFORM to match the detected hardware: "<DETECTED_PLATFORM>".DETECTED_PLATFORM is non-empty and matches HSB_PLATFORM → no change needed. Confirm:
Platform verified: <HSB_PLATFORM> matches hardware (product_name: <DETECTED_PRODUCT>).DETECTED_PLATFORM is empty (file missing, unreadable, or unrecognized product name) and HSB_PLATFORM is set → keep the existing HSB_PLATFORM. Warn:
Could not auto-detect platform from hardware (product_name: "<DETECTED_PRODUCT>").
Keeping existing HSB_PLATFORM: "<HSB_PLATFORM>".Both DETECTED_PLATFORM and HSB_PLATFORM are empty → fall through to the manual platform question in step 5.
After reconciliation, persist the updated HSB_PLATFORM in the remote session state file so subsequent phases use the correct value.
When this skill is used from Linux/Windows with a local Claude Code session that shells out to SSH, prefer these environment variables when present:
SSH_TARGET for the remote login target such as nvidia@agx-thor-hostREMOTE_ROOT for the remote working directory where the repo should liveREMOTE_SUDO for privileged commands. Accept sudo, sudo -n, or empty stringREMOTE_SSH_OPTS for additional SSH optionsHSB_PLATFORM as an optional platform hintHSB_REPO for a custom GitHub repository URL to clone (e.g. https://github.com/myorg/my-hsb-fork.git). If not set, defaults to https://github.com/nvidia-holoscan/holoscan-sensor-bridge.gitIf these are set, notify the user of these settings and use them without re-asking unless the user explicitly overrides them.
Before Phase 1, print the resolved remote execution settings you will use, with secrets redacted if needed.
Present the phase plan from Gate 2 above before making any changes. Skip Phase 3 for non-Thor platforms.
Then execute one phase at a time.
After each non-final phase (Phases 0–5):
--verbose mode (see "Verbosity mode" section):Proceed to Phase <N+1>? [Y/n] while specifing what is phase N+1 and wait for confirmation before continuing (see "Phase gate" section).If something fails, do not just dump raw logs. Summarize:
This phase is mandatory and must run before any SSH connection, repo clone, package/configuration check, container build, reboot, or devkit setting change.
Estimate the full-run token budget for the entire setup workflow, not just the next phase. The values below are conservative heuristics, not measured historical usage. Treat them as initial safety budgets and refine them from actual /hsb-setup run logs once measured token usage is available:
--verbose, custom repo handling, SSH key remediation, reboot recovery, or extra troubleshooting is expected.Check remaining usage using the best available Claude Code/account usage source for the current subscription plan. Prefer machine-readable or product-provided usage data when available. If no reliable usage source is available, ask the user to provide their current remaining usage/quota from the Claude Code account or plan UI.
When asking the user because usage cannot be self-verified, present the options in this exact order so the safe stop choices appear first:
Do not put the proceed option first. The user must intentionally move past the stop choices before selecting proceed.
Display the result to the user before continuing:
Token-budget preflight
- Estimated tokens required for complete /hsb-setup run: <estimate>
- Estimate basis: conservative heuristic; refine from actual run logs when available
- Safety margin included: <margin>
- Remaining plan usage available: <available or "unverified">
- Result: PASS / FAILStop on insufficient or unverifiable budget:
--y must not bypass this preflight.Ask only the minimum required questions:
sudo for network and Docker setup?If the user already provided any of these, do not ask again.
| Script | Purpose | Arguments |
|---|---|---|
scripts/hsb_phase_runner.sh | Structured shell execution with timestamped logs per phase | <phase_name> <command> |
Use run_script(scripts/hsb_phase_runner.sh, <phase_name>, <command>) to run phase steps with automatic logging.
See references/phase-details.md for full step-by-step phase instructions, output style, verbosity behavior, auto-approve mode, phase gate rules, and the persistent SSH session model.
Try these fixes in order when applicable:
git-lfs, Docker access, xhost, network route).--help)If $ARGUMENTS contains --help or -h, do not run the workflow. Instead, print the following help text verbatim and stop:
Holoscan Sensor Bridge — Demo Bring-Up Skill
USAGE
/hsb-setup [PLATFORM] [OPTIONS]
PLATFORM (optional — will prompt if omitted)
AGX Orin NVIDIA Jetson AGX Orin (iGPU, build with --igpu)
AGX Thor NVIDIA Jetson AGX Thor (iGPU, build with --igpu)
IGX Orin iGPU NVIDIA IGX Orin in iGPU configuration (build with --igpu)
IGX Orin dGPU NVIDIA IGX Orin with discrete GPU (build with --dgpu)
DGX Spark NVIDIA DGX Spark (iGPU, build with --igpu)
OPTIONS
--help, -h Show this help message and exit
--verbose Show full raw command output for every phase
(default is concise bullet-point summaries)
--y Auto-approve all phase gates (skip user confirmation
between phases). Not recommended — a confirmation
warning is shown before proceeding. All output is
saved to a timestamped log file.
--repo <URL> Clone a custom GitHub repo instead of the default
nvidia-holoscan/holoscan-sensor-bridge.
Can also be set via the HSB_REPO env var.
Priority: --repo flag > HSB_REPO env var > default repo
ENVIRONMENT VARIABLES (set before invoking the skill)
SSH_TARGET Remote login target (e.g. ubuntu@10.0.0.1)
REMOTE_ROOT Remote working directory for repo clone and builds
REMOTE_SUDO Privilege escalation: 'sudo', 'sudo -n', or ''
REMOTE_SSH_OPTS Additional SSH options (e.g. -o ServerAliveInterval=30)
HSB_PLATFORM Platform hint (same values as PLATFORM above)
HSB_REPO Custom GitHub repo URL (overridden by --repo flag)
WORKFLOW PHASES
Phase 0 Token-budget preflight; verify enough plan usage for a full run
Phase 1 Confirm platform, clone repo, and study user guide
Phase 2 Host prerequisite checks and network setup
Phase 3 Native build of CLI tools (AGX Thor only, skipped otherwise)
Phase 4 Build, run demo container, and verify connectivity
Phase 5 Produce issues report, optionally export to file
Phase 6 Stop apps, exit container, hand off to user
The skill prompts for confirmation between each phase.
EXAMPLES
/hsb-setup AGX Thor
/hsb-setup AGX Thor --verbose
/hsb-setup AGX Thor --y
/hsb-setup IGX Orin dGPU --repo https://github.com/myorg/my-fork.git
/hsb-setup --helpAfter printing the help text, do not proceed with any phases or ask any questions.
See the EXAMPLES section in Built-in help (--help) for invocation examples.
When $ARGUMENTS contains a platform, use it instead of asking again. Strip --verbose, --y, --repo <URL>, and --help from the arguments before parsing the platform name.
© 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 14 other files (scripts, references) in skills/hsb-setup of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
Hsb Setup 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 |
|---|---|---|---|---|---|---|
| Hsb Setup this skillNVIDIA/skills | 3.5k | — | ~4.9k | Automated safety check: Notes | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| NEAR AI Cloud Private Inferenceinternet-court/internet-court-skill | 6.4k | 2 repos | ~1.3k | Automated safety check: Pass | Custom licence | |
| Nemoclaw Maintainer Normalize Title TagsNVIDIA/NemoClaw | 23k | — | ~693 | Automated safety check: Pass | Apache-2.0 |
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
internet-court/internet-court-skill
Shows how to call NEAR AI Cloud through an OpenAI-compatible API and verify that inference ran in a TEE, using attestation checks and signed chat responses.
NVIDIA/NemoClaw
Remove bracketed NemoClaw tags from GitHub issue and PR titles.
NVIDIA/Megatron-LM
Walks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI.
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
Clone the latest NVIDIA Holoscan Sensor Bridge repo, ask which supported devkit is being used, configure the host per platform, build the correct demo container, run it, and verify HSB connectivity…. Hsb Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization.2.
Hsb Setup fits situations like: holoscan Sensor Bridge setup; container launch; first-connectivity bring-up.
Run `npx skills add NVIDIA/skills --skill hsb-setup -a claude-code`. Or copy the skill folder (skills/hsb-setup in NVIDIA/skills) into .claude/skills/hsb-setup in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill hsb-setup -a codex`. Or copy the skill folder (skills/hsb-setup in NVIDIA/skills) into .agents/skills/hsb-setup 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 hsb-setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hsb-setup, .gemini/skills/hsb-setup, .github/skills/hsb-setup and .opencode/skills/hsb-setup in your project.
Going by SKILL.md and its folder, Hsb Setup needs a shell, PowerShell and Windows cmd for the scripts in its folder and the command-line tools its instructions call (sh). Our summary lists: A Bash shell; PowerShell; Docker. Its frontmatter pre-approves these tools: Read, Write, Edit, MultiEdit, Grep, Glob, Bash.
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Hsb Setup 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 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Hsb Setup: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Embeddings via 9Router (decolua/9router, 30k stars) and NEAR AI Cloud Private Inference (internet-court/internet-court-skill, 6.4k 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 3,534 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.