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/doca-gpunetio-ib-write-bw

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

Use this skill when the user is building, running, or interpreting the doca/tools/gpunetio_ib_write_bw client+server benchmark — a CUDA kernel on the client posts RDMA WRITE work requests through the doca-gpunetio device-side surface to measure sustained GPU-driven WRITE bandwidth on a GPU+IB-device pair. Trigger even when the user does not explicitly mention "doca-gpunetio-ib-write-bw" or "GPUNetIO" — typical implicit phrasings include "measure WRITE BW when the GPU posts the WRs", "BW swings between runs on the same flags", "is the NIC saturated or am I CPU-bound on the CUDA kernel", "meson compile fails for the GPUNetIO bw tool", "nvidia_peermem isn't picking up my GPU buffer", or "GPU-initiated WRITE throughput vs CPU-initiated perftest". Refuse and route elsewhere for general doca-gpunetio library work, DOCA install, the GPU-initiated WRITE latency analog, the CPU-initiated upstream perftest, or application-level end-to-end throughput — those belong to other skills.

Use this Skill: https://skilld.dev/gh/nvidia/skills/doca-gpunetio-ib-write-bw

This session only. Nothing lands on disk.

BENCHMARK.md

≈1k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Evaluation Report

Evaluation of the doca-gpunetio-ib-write-bw skill before publication through Skill Evaluator.

This benchmark summarizes 3-Tier Evaluation from Skill Evaluator results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.

Evaluation Summary

  • Skill: doca-gpunetio-ib-write-bw
  • Evaluation date: 2026-07-26
  • Environment: k8s-sandbox
  • Dataset: 4 evaluation tasks
  • Attempts per task: 1
  • Pass threshold: 50%
  • Overall verdict: PASS

Agents Used

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8)
  • Codex (openai/openai/gpt-5.5)

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 (Security): checks for unsafe operations, secret leakage, and unauthorized access.
  • skill_execution (Skill Execution): verifies that the agent loaded the expected skill and workflow.
  • skill_efficiency (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.
  • accuracy (Accuracy): grades final-answer correctness against the reference answer.
  • goal_accuracy (Goal Accuracy): checks whether the overall user task completed successfully.
  • behavior_check (Behavior Check): verifies expected behavior steps, including safety expectations.

Test Tasks

The benchmark dataset contained 4 evaluation tasks:

  • Positive tasks: 3 tasks where the skill was expected to activate.
  • Negative tasks: 1 tasks where no skill was expected.
  • Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.

Task composition is derived from the evaluation dataset when possible. Entries with expected_skill set are treated as positive skill-activation cases, while entries with expected_skill: null are treated as negative activation cases.

Results

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% (+65%) 100% (+40%)
Discoverability 4 97% (+34%) 95% (+50%)
Effectiveness 4 81% (+60%) 88% (+62%)
Efficiency 4 94% (+44%) 100% (+58%)

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.

Tier 1: Static Validation Summary

Tier 1 validation passed with observations. Skill Evaluator ran 1 checks and found 7 total findings.

Top findings:

  • MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (skills/tools/doca-gpunetio-ib-write-bw)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/tools/doca-gpunetio-ib-write-bw/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/tools/doca-gpunetio-ib-write-bw/SKILL.md)
  • MEDIUM SCHEMA/author_missing: Author not specified in metadata (skills/tools/doca-gpunetio-ib-write-bw/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'CAPABILITIES.md' in skill root (skills/tools/doca-gpunetio-ib-write-bw/CAPABILITIES.md)

Tier 2: Deduplication Summary

This tier was not run or did not produce findings in this report.

Publication Recommendation

The skill is suitable to proceed toward Skill Evaluator publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.

Source: SKILL.md on GitHub

No alerts2mo3 checks · Risk SAFE
  • Gen Agent Trust Hub2mo

    The skill is a set of instructions and documentation for building and running the gpunetio_ib_write_bw benchmark tool. No security vulnerabilities or malicious patterns were detected. All external references point to official NVIDIA resources and the skill includes guidance on redacting sensitive diagnostic information.

  • 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 on Linux with a BlueField DPU or ConnectX NIC, NVIDIA GPU, CUDA toolkit and nvcc, loaded `nvidia_peermem`, and an InfiniBand RNIC paired with the GPU. Uses `pkg-config` for doca-gpunetio, doca-rdma, and doca-common, plus the installed gpunetio_ib_write_bw sources. Run only on a trusted, non-shared IB fabric during the benchmark window.

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