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/doca-caps

@f64fa0e
by NVIDIA Corporationnvidia/skills3.5k stars
424

Use this skill when the user wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for a debug session or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this box", "is my BlueField PF visible to DOCA", "is Flow available on my RHEL host", "enumerate VF representors for pf0", "doca_caps: command not found", or "empty output for RDMA, is the tool broken". Refuse and route elsewhere for DOCA installation, library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary — those belong to other skills.

Use this Skill: https://skilld.dev/gh/nvidia/skills/doca-caps

This session only. Nothing lands on disk.

BENCHMARK.md

≈908 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Evaluation Report

Evaluation of the doca-caps skill before publication through NVSkills-Eval.

This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval 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-caps
  • Evaluation date: 2026-07-15
  • NVSkills-Eval profile: external
  • Environment: astra-sandbox
  • Dataset: 8 evaluation tasks
  • Attempts per task: 1
  • Pass threshold: 50%
  • Overall verdict: PASS

Agents Used

  • claude-code
  • codex

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.
  • token_efficiency (Token Efficiency): compares token usage with and without the skill.

Test Tasks

The benchmark included 8 recorded Tier 3 trials, but the source evaluation dataset was not available in this report payload.

Results

Dimension Num claude-code codex
Security 4 100% (+0%) 100% (+0%)
Correctness 4 100% (+50%) 96% (+39%)
Discoverability 4 100% (+53%) 89% (+36%)
Effectiveness 4 90% (+60%) 94% (+58%)
Efficiency 4 93% (+37%) 83% (+26%)

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. NVSkills-Eval ran 1 checks and found 7 total findings.

Top findings:

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

1 warning2mo3 checks · Risk SAFE
  • Gen Agent Trust Hub2mo

    The skill provides guidance for using the NVIDIA DOCA doca_caps CLI tool to inspect hardware and library capabilities. It follows read-only principles and utilizes official vendor resources.

  • Socket2mo

    No alerts

  • Snyk2mo

    Risk: MEDIUM · 1 issue

Signed by skilld at f64fa0e. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 3 months ago
metadata
{
  "kind": "tool"
}
Other metadata
compatibility
Requires DOCA SDK ≥ 2.6.0 installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC; runs identically on the host or on the BlueField Arm side. Invokes /opt/mellanox/doca/tools/doca_caps and reads `pkg-config doca-common` to confirm the install.

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