Description: <br>
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. <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 engineers use this skill to sanity-check a running AutoMagicCalib stack with the bundled sample dataset, verifying end-to-end calibration works before running on real data. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [No] <br> Credential Type(s): [None] <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>
- AMC Setup Calibration Stack Skill <br>
- AMC Run Video Calibration Skill <br>
- AMC Run RTSP Calibration Skill <br>
Skill Output: <br>
Output Type(s): [Shell commands, API Calls, 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>
5 evaluation tasks (3 positive, 2 negative) in isolated sandbox pods. <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 found and executed. <br>
- Effectiveness: Checks whether the user's goal was achieved and expected workflow behavior was followed. <br>
- Efficiency: Checks routing quality, workspace-aware skill reads, and productive tool use. <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 found and executed. <br>skill_efficiency: Routing quality, workspace-aware skill reads, and productive tool use. <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>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 66% → 99% (+33 points) | 64% → 90% (+27 points) |
| Security | 100% → 100% (±0 points) | 80% → 70% (-10 points) |
| Correctness | 36% → 100% (+64 points) | 48% → 100% (+52 points) |
| Discoverability | 69% → 100% (+31 points) | 62% → 92% (+30 points) |
| Effectiveness | 57% → 100% (+43 points) | 59% → 93% (+34 points) |
| Efficiency | 68% → 95% (+27 points) | 69% → 95% (+27 points) |
Testing Completed: <br>
[x] Agent Red-Teaming <br> [ ] Network Security <br> [ ] Product Security <br>
Skill Version(s): <br>
1.0.0 (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>