DeepStream SOP compliance monitoring — 18-section reference covering GPU-accelerated event boundary detection (GEBD, e.g. DDM) + VLM-based SOP checking with Cosmos Reason 1 or 2, Triton CAPI, Basler GigE camera (Pylon SDK + emulation), Kafka/SSE output, and RTSP streaming output.
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin deepstream-sop in LeoUnahur/agente-pyme-unahur. Commands read from the repo's manifest — 1 month ago.
deepstream-sopUse 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. Trig
NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
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Build DeepStream GStreamer pipelines interactively. Collects input source, inference, tracker, and output preferences, then assembles a ready-to-run gst-launch-1.0 pipeline using BM25 retrieval over 270+ verified pipelines (zero external dependencies).
1 skill
Plan and configure repo-native Nemotron customization workflows from existing steps: curate/nemo_curator JSONL cleaning, translate/nemo_curator corpus translation, sft/automodel, peft/automodel, sft/megatron_bridge, peft/megatron_bridge, pretrain/CPT, rl/nemo_rl alignment, byob/mcq benchmarks, convert/megatron_to_hf and other checkpoint conversion, optimize/modelopt, eval/model_eval, env/env_toml profiles, and end-to-end pipelines.
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Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you find the right skills to help in building NVIDIA-powered workflows.
1 skill
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