Agent Skills / Example applications

HSB · Agent Skill

Example applications

Discover and run Holoscan Sensor Bridge example applications on a connected devkit. Filters available apps by the user's platform, HSB software version, board type, and sensors. Supports timed execution, failure analysis, code-edit suggestions, and iterative re-runs.

Install

npx skills add nvidia/skills --skill hsb-app

Add --agent cursor, --agent claude-code, --agent codex, or --agent kiro-cli to target a specific agent.

At a glance

Skill	hsb-app
Product	Holoscan Sensor Bridge
Version	1.0.0
License	Apache-2.0
Tags	holoscan-sensor-bridge, hsb, running-app
Source	NVIDIA/skills ↗

This skill is ready for commercial/non-commercial use.

Owner

NVIDIA

Use Case

Developers and engineers use this skill to discover, select, and run Holoscan Sensor Bridge example applications on devkits with connected HSB boards, including failure analysis and iterative debugging.

Deployment Geography for Use

Global

Known Risks and Mitigations

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills.
Mitigation: Review and scan skill before deployment.

Reference(s)

• Phase Details
• Agent Skills Specification

Skill Output

Output Type(s): [Shell commands, Configuration instructions, Analysis]
Output Format: [Markdown with inline bash code blocks]
Output Parameters: [1D]
Other Properties Related to Output: [None]

Evaluation Agents Used

• Claude Code (claude-code)
• Codex (codex)

Evaluation Tasks

Evaluated against 3 internal evaluation tasks (all positive skill-activation cases, 2 attempts per task, 50% pass threshold).

Evaluation Metrics Used

Reported benchmark dimensions:

• Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
• Correctness: Checks whether the agent follows the expected workflow and produces the correct final output.
• Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant.
• Effectiveness: Checks whether the agent performs measurably better with the skill than without it.
• Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work.

Underlying evaluation signals used in this run:

• security: Checks for unsafe operations, secret leakage, and unauthorized access.
• skill_execution: Verifies that the agent loaded the expected skill and workflow.
• skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage.
• accuracy: Grades final-answer correctness against the reference answer.
• goal_accuracy: Checks whether the overall user task completed successfully.
• behavior_check: Verifies expected behavior steps, including safety expectations.
• token_efficiency: Compares token usage with and without the skill.

Evaluation Results

Dimension	Num	claude-code	codex
Security	6	100% (+17%)	100% (+17%)
Correctness	6	95% (+0%)	84% (+41%)
Discoverability	6	73% (-1%)	69% (+16%)
Effectiveness	6	88% (+4%)	76% (+66%)
Efficiency	6	59% (+0%)	60% (+22%)

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

(For Release on NVIDIA Platforms Only)
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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