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/doca-socket-relay

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

Use this skill when the operator is driving the DOCA Socket Relay to bridge a socket-oriented host application onto a BlueField DPU peer without rewriting it — picking the deployment shape (in-process, sidecar, or BlueField service container), configuring the host-side socket and the DPU-side forwarding endpoint, walking the bind → connect → round-trip → admit-fleet smoke, or diagnosing a stuck/silent relay. Trigger even when the user does not explicitly mention "DOCA Socket Relay" — typical implicit phrasings include "move my socket app onto the BlueField without rewriting it", "host app gets ECONNREFUSED on the relay", "relay accepts the connection but bytes never arrive on the DPU side", "first round-trip works, the rest hang", "bridge an AF_UNIX (UDS) socket to a DPU peer over Comch", or "I want a sidecar that forwards my socket to the BlueField". Refuse and route elsewhere for the comch programming API, line-rate raw packet I/O via doca-eth, and DOCA install/bring-up — those belong to other skills.

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

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-socket-relay 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-socket-relay
  • 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% (+50%) 100% (+20%)
Discoverability 4 100% (+25%) 95% (+34%)
Effectiveness 4 81% (+62%) 92% (+52%)
Efficiency 4 94% (+33%) 93% (+55%)

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-socket-relay)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/tools/doca-socket-relay/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/tools/doca-socket-relay/SKILL.md)
  • MEDIUM SCHEMA/author_missing: Author not specified in metadata (skills/tools/doca-socket-relay/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'CAPABILITIES.md' in skill root (skills/tools/doca-socket-relay/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 configuration and operational guide for the NVIDIA DOCA Socket Relay. It provides structured workflows for deploying the relay in different environments, conducting smoke tests, and diagnosing connection issues. The analysis found no evidence of malicious patterns, obfuscation, or unauthorized data exfiltration. All external resources and commands are consistent with the vendor's documented infrastructure.

  • Socket2mo

    No alerts

  • Snyk2mo

    Risk: LOW · No issues

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 installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU attached, plus the socket-oriented host application that will be migrated onto the fabric. Container-shape deployment additionally relies on the BlueField kubelet-standalone runtime per doca-container-deployment.

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