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/deepstream-import-vision-model

@6d03410
by NVIDIA Corporationnvidia/skills3.5k stars
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Use this skill to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.

Use this Skill: https://skilld.dev/gh/nvidia/skills/deepstream-import-vision-model

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: deepstream-import-vision-model

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: deepstream-import-vision-model
  • Evaluation date: 2026-08-13
  • Evaluator version: 1.2.4
  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
  • Tasks: 13 evaluation tasks (13 positive)
  • Dataset digest: sha256:09a6b1b8629eb5bcf468be26c1ee2aafdbd85a4b31fa501b9a6aab41a1ff76f1 (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 44% → 72% (+27 points) 46% → 61% (+15 points)
Security 58% → 54% (-4 points) 35% → 42% (+8 points)
Correctness 42% → 86% (+45 points) 65% → 72% (+8 points)
Discoverability 51% → 88% (+37 points) 50% → 72% (+22 points)
Effectiveness 24% → 43% (+19 points) 28% → 34% (+6 points)
Efficiency 48% → 86% (+38 points) 54% → 84% (+30 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); 13 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/deepstream-import-vision-model/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'install.ps1' in skill root (skills/deepstream-import-vision-model/install.ps1)
  • LOW SCHEMA/unexpected_file: Unexpected 'install.sh' in skill root (skills/deepstream-import-vision-model/install.sh)
  • LOW SCHEMA/unexpected_file: Unexpected 'setup.sh' in skill root (skills/deepstream-import-vision-model/setup.sh)
  • LOW SCHEMA/unexpected_file: Unexpected 'CHANGELOG.md' in skill root (skills/deepstream-import-vision-model/CHANGELOG.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

No alerts27d3 checks · Risk SAFE
  • Gen Agent Trust Hub27d

    This skill provides an automated pipeline for importing HuggingFace or NVIDIA NGC vision models into NVIDIA DeepStream. It follows industry best practices for model acquisition, TensorRT engine building, and benchmarking. All external downloads target trusted sources (HuggingFace and NVIDIA NGC) or well-known package registries, and all code traces back to the vendor (NVIDIA). No malicious patterns or data exfiltration risks were detected.

  • Socket27d

    No alerts

  • Snyk27d

    Risk: LOW · No issues

Signed by skilld at 6d03410. 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
Other metadata
metadata
{
  "author": "Tushar Khinvasara <tkhinvasara@nvidia.com>",
  "owner": "Tushar Khinvasara <tkhinvasara@nvidia.com>",
  "service": "deepstream",
  "version": "1.5.2",
  "reviewed": "2026-08-04",
  "team": "deepstream-sdk",
  "tags": [
    "deepstream",
    "tensorrt",
    "object-detection",
    "import-vision-model"
  ],
  "languages": [
    "bash",
    "python",
    "cpp"
  ],
  "domain": "computer-vision"
}

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