All skills
nvidia avatar

/doca-telemetry-exporter

@36dd834
by NVIDIA Corporationnvidia/skills3.5k stars
427

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. Trigger even when the user does not explicitly mention "DOCA Telemetry Exporter" or "doca_telemetry_exporter_*" — typical implicit phrasings include "publishing counters from my DOCA app", "BAD_STATE when I report an event", "consumer/DTS sees nothing but my report succeeded", "how do I export NetFlow/IPFIX records", or "should I link the exporter or the telemetry service". Refuse and route elsewhere for the receiving DOCA Telemetry Service (DTS), plain stdout logging via doca_log, or real-time event subscription back into the app via doca-comch — those belong to other skills.

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

This session only. Nothing lands on disk.

BENCHMARK.md

≈1.3k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Skill Benchmark: doca-telemetry-exporter

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: doca-telemetry-exporter
  • Evaluation date: 2026-07-29
  • Evaluator version: 0.9.0
  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
  • Tasks: 4 evaluation tasks (3 positive, 1 negative)
  • Dataset digest: sha256:bdb053d3eebd3787bba933e197d64385efe318e5e5666e43c6300e91e2c8f42e (skill-evaluator-dataset-snapshot/1)
  • Attempts per task: 1
  • Environment: k8s-sandbox
  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;
  • produces correct answers;
  • is discovered and activated when needed;
  • helps the agent complete the user's goal and expected workflow; and
  • avoids wasted skill and tool usage.

Results at a Glance

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)

How to read this table: baseline is the same task attempted without the target skill. Uplift is skill score - baseline score, shown in percentage points.

Example: 47% → 92% (+45 points) means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.

Tier Status

Tier Purpose Status Evidence
Tier 1 Static validation PASSED WITH OBSERVATIONS 1 validator(s); 7 finding(s)
Tier 2 Semantic deduplication NOT RUN No result was recorded
Tier 3 Live agent evaluation PASS 2 agent(s); 4 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (skills/libs/doca-telemetry-exporter)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/libs/doca-telemetry-exporter/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/libs/doca-telemetry-exporter/SKILL.md)
  • MEDIUM SCHEMA/author_missing: Author not specified in metadata (skills/libs/doca-telemetry-exporter/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'CAPABILITIES.md' in skill root (skills/libs/doca-telemetry-exporter/CAPABILITIES.md)
  • 2 additional finding(s) are available in the full evaluation artifacts.
</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
Dimension Question Scored signals
Security Is it safe to use? security (100%)
Correctness Is the answer correct? accuracy (100%)
Discoverability Was the right skill loaded when needed? skill_execution (100%)
Effectiveness Did the skill help complete the task? goal_accuracy (50%) + behavior_check (50%)
Efficiency Did it avoid wasted tool or skill usage? skill_efficiency (100%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
  • Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.
  • skill_execution (Skill Execution): whether the expected skill was found and executed.
  • skill_efficiency (Efficiency): routing quality, workspace-aware skill reads, and productive tool use.
  • accuracy (Accuracy): final-answer correctness against the reference answer.
  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.
</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Source: SKILL.md on GitHub

No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill 'doca-telemetry-exporter' is a safe, documentation-only resource for NVIDIA DOCA developers. It provides guidance on telemetry event publishing without shipping executable code or dangerous instructions.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Signed by skilld at 36dd834. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 1 hour ago.

Activeupdated 2 months ago
metadata
{
  "kind": "library"
}
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 or ConnectX NIC attached. Reads the user's local install via `pkg-config doca-telemetry-exporter` and inspects /opt/mellanox/doca/{lib,include,samples,applications}.

README badge

README badge for nvidia/skills/doca-telemetry-exporter