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/doca-flow-dpa-perf

@a5736e4
by NVIDIA Corporationnvidia/skills3.5k stars
424

Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

Use this Skill: https://skilld.dev/gh/nvidia/skills/doca-flow-dpa-perf

This session only. Nothing lands on disk.

skill-card.md

≈1.1k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Description: <br>

Use this skill when invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path. <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 operators, performance engineers, DOCA Flow application developers, and AI agents who need a defensible measurement of the DPA-offloaded Flow update path on DPA-capable hardware (ConnectX-7+, BlueField-3+). <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 4 evaluation tasks (3 positive skill-activation, 1 negative). <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

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

Underlying evaluation signals used in this run: <br>

  • security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
  • skill_execution: Verifies that the agent loaded the expected skill and workflow. <br>
  • skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage. <br>
  • accuracy: Grades final-answer correctness against the reference answer. <br>
  • goal_accuracy: Checks whether the overall user task completed successfully. <br>
  • behavior_check: Verifies expected behavior steps, including safety expectations. <br>

Evaluation Results: <br>

Dimension Num Claude Code (aws/anthropic/bedrock-claude-opus-4-8) Codex (openai/openai/gpt-5.5)
Security 4 100% (+0%) 100% (+0%)
Correctness 4 100% (+75%) 100% (+55%)
Discoverability 4 100% (+25%) 95% (+33%)
Effectiveness 4 100% (+75%) 92% (+67%)
Efficiency 4 92% (+26%) 81% (+50%)

Skill Version(s): <br>

2790d51 (source: git SHA, committed 2026-07-26) <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>

Source: SKILL.md on GitHub

No alerts2mo3 checks · Risk SAFE
  • Gen Agent Trust Hub2mo

    The skill provides guidance for using the `doca_flow_dpa_perf` CLI tool to measure network performance on DPA-capable hardware. It is purely instructional and does not contain malicious code, remote downloads from untrusted sources, or obfuscation.

  • Socket2mo

    No alerts

  • Snyk2mo

    Risk: LOW · No issues

Signed by skilld at a5736e4. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 months ago
metadata
{
  "kind": "tool"
}
Other metadata
compatibility
Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a DPA-capable device attached — ConnectX-7 as the minimum supported ConnectX generation, ConnectX-8 recommended, or BlueField-3 (BlueField-2 and earlier ConnectX are unsupported). VNF Flow mode required; PF or VF only (SFs are not supported on the DPA path). Reads `pkg-config doca-flow` and the shipped `doca_flow_dpa_perf` binary plus its README on the user's install.

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