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/deepstream-sop

@4cb1092
by NVIDIA Corporationnvidia/skills3.5k stars
424

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

Ask your Agent

Use this Skill: https://skilld.dev/gh/nvidia/skills/deepstream-sop

This session only. Nothing lands on disk.

BENCHMARK.md

≈867 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Evaluation Report

Evaluation of the deepstream-sop 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: deepstream-sop
  • Evaluation date: 2026-07-13
  • NVSkills-Eval profile: internal
  • Environment: astra-sandbox
  • Dataset: 10 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 10 recorded Tier 3 trials, but the source evaluation dataset was not available in this report payload.

Results

Dimension Num claude-code codex
Security 5 100% (+0%) 90% (+0%)
Correctness 5 93% (+34%) 83% (+19%)
Discoverability 5 95% (+34%) 88% (+19%)
Effectiveness 5 73% (+28%) 67% (+21%)
Efficiency 5 80% (+21%) 83% (+18%)

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 3 total findings.

Top findings:

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (team-skills/deepstream/deepstream-sop/SKILL.md)
  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (team-skills/deepstream/deepstream-sop/SKILL.md)
  • LOW SCHEMA/unexpected_file: Unexpected 'configs' in skill root (team-skills/deepstream/deepstream-sop/configs)

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

Third-party checks

2 warnings27d3 checks · Risk MEDIUM
  • Gen Agent Trust Hub27d

    This skill provides a framework for building an industrial SOP monitoring microservice. It uses multi-stage processing, including Vision-Language Models and custom logic. Security observations include dynamic code generation based on configuration files, execution of build scripts via subprocess, and reliance on external research repositories for model weights and code.

  • Socket27d

    2 alerts: gptSecurity, gptAnomaly

  • Snyk27d

    Risk: LOW · No issues

Provenance

Signed by skilld at 4cb1092. 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

Capability

owner
windy@nvidia.com
service
deepstream-sop
version
1.0.0
Other metadata
reviewed
2026-04-08
metadata
{
  "author": "Wind Yuan <windy@nvidia.com>",
  "tags": [
    "deepstream",
    "sop",
    "vlm",
    "triton",
    "gpu"
  ],
  "languages": [
    "python"
  ],
  "frameworks": [
    "deepstream",
    "triton",
    "fastapi"
  ],
  "domain": "video-analytics"
}

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