Description: <br>
Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache-2.0 <br>
Use Case: <br>
Developers and robotics engineers fine-tune GR00T or openpi policy models on LeRobot datasets for healthcare robotics workflows. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
Known Risks and Mitigations: <br>
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>
Reference(s): <br>
Skill Output: <br>
Output Type(s): [Shell commands, Configuration instructions, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>
Evaluation Agents Used: <br>
- Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) <br> - Codex (
openai/openai/gpt-5.5) <br>
Evaluation Tasks: <br>
Evaluated against 2 internal tasks (2 positive) with 3 attempts each in k8s-sandbox. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
- Security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
- Correctness: Checks final-answer correctness against the reference answer. <br>
- Discoverability: Checks whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>
- Effectiveness: Checks whether the user's goal was achieved and the expected workflow behavior was followed. <br>
- Efficiency: Checks tool-call productivity and token efficiency. <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>accuracy: Final-answer correctness against the reference answer. <br>goal_accuracy: Whether the user's goal was achieved. <br>behavior_check: Whether the expected workflow behavior was followed. <br>skill_efficiency: Tool-call productivity. <br>token_efficiency: Actual uncached prompt plus completion token usage. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 75.2% | 60.9% |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 25.0% (-75.0 points) |
| Correctness | 10.0% → 70.0% (+60.0 points) | 3.3% → 100.0% (+96.7 points) |
| Discoverability | 95.0% | 81.3% |
| Effectiveness | 5.0% → 35.0% (+30.0 points) | 5.0% → 28.8% (+23.8 points) |
| Efficiency | 76.1% | 69.5% |
Skill Version(s): <br>
0.8.0 (source: frontmatter) <br>
Ethical Considerations: <br>
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. <br>
(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>