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/cuopt-install

@ca9a15e
by NVIDIA Corporationnvidia/skills3.5k stars
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Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.

Use this Skill: https://skilld.dev/gh/nvidia/skills/cuopt-install

This session only. Nothing lands on disk.

BENCHMARK.md

≈1.2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Skill Benchmark: cuopt-install

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: cuopt-install
  • Evaluation date: 2026-08-12
  • Evaluator version: 1.2.4
  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
  • Tasks: 7 evaluation tasks (7 positive)
  • Dataset digest: sha256:69515ef8677f7773d6ae7ac097b3a02767971b308d79ba74759f647dd33794c5 (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 56% → 90% (+34 points) 63% → 91% (+28 points)
Security 93% → 86% (-7 points) 100% → 100% (±0 points)
Correctness 86% → 94% (+9 points) 89% → 100% (+11 points)
Discoverability 27% → 96% (+69 points) 47% → 94% (+46 points)
Effectiveness 64% → 81% (+18 points) 76% → 93% (+17 points)
Efficiency 12% → 93% (+81 points) 3% → 67% (+63 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); 5 finding(s)
Tier 2 Semantic deduplication NOT RUN No result was recorded
Tier 3 Live agent evaluation PASS 2 agent(s); 7 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • MEDIUM SCHEMA/frontmatter_field_placement: Root field 'version' is ignored; use 'metadata.version' (skills/cuopt-install/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/cuopt-install/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/cuopt-install/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'benchmark' in skill root (skills/cuopt-install/benchmark)
  • LOW SCHEMA/author_format: Author must be of the form 'Name <email@host>' (skills/cuopt-install/SKILL.md)
</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

1 warning1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill provides standard installation and verification procedures for NVIDIA cuOpt using trusted vendor resources (NVIDIA PyPI, Conda, and Docker repositories). No malicious patterns, obfuscation, or unauthorized data exfiltration attempts were detected. All findings are consistent with the skill's primary purpose of software deployment and diagnostic verification.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: MEDIUM · 1 issue

Signed by skilld at ca9a15e. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated last month
version
26.10.00
Other metadata
metadata
{
  "author": "NVIDIA cuOpt Team",
  "tags": [
    "cuopt",
    "install",
    "deployment",
    "python",
    "server"
  ]
}

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