Description: <br>
Use this skill when the user is doing hands-on DOCA Telemetry Exporter programming on a host where DOCA is installed — defining a doca_telemetry_exporter_schema and event types, creating sources, picking a publish surface (typed events / opaque events / the metrics counter-gauge-histogram API / OTLP logs / NetFlow), walking the schema-then-source lifecycle, or debugging DOCA_ERROR_* failures from the exporter API. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache 2.0 AND CC-BY-4.0 <br>
Use Case: <br>
External developers and engineers building DOCA applications that emit structured telemetry (counters, gauges, events) through the DOCA Telemetry Exporter C library to an external consumer. <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>
Skill Output: <br>
Output Type(s): [Code, Shell commands, Configuration instructions] <br> Output Format: [Markdown with inline 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>
4 evaluation tasks (3 positive, 1 negative) from skill-evaluator-dataset-snapshot/1, each in an isolated sandbox pod. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
- Security: Whether the skill is safe to use (unsafe operations, secret leakage, unauthorized access). <br>
- Correctness: Whether the answer produced is correct against the reference answer. <br>
- Discoverability: Whether the right skill was loaded and activated when needed. <br>
- Effectiveness: Whether the skill helped complete the user's goal and expected workflow. <br>
- Efficiency: Whether the skill avoided wasted tool or skill usage. <br>
Underlying evaluation signals used in this run: <br>
security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Verifies the expected skill was found and executed. <br>skill_efficiency: Measures routing quality, workspace-aware skill reads, and productive tool use. <br>accuracy: Checks final-answer correctness against the reference answer. <br>goal_accuracy: Verifies whether the user's goal was achieved. <br>behavior_check: Verifies whether the expected workflow behavior was followed. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 57% → 98% (+40 points) | 64% → 97% (+34 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 40% → 100% (+60 points) | 75% → 100% (+25 points) |
| Discoverability | 62% → 100% (+38 points) | 59% → 92% (+33 points) |
| Effectiveness | 30% → 94% (+64 points) | 54% → 94% (+40 points) |
| Efficiency | 53% → 94% (+41 points) | 30% → 100% (+70 points) |
Skill Version(s): <br>
0aeaedb (source: git SHA, committed 2026-07-28) <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>