Agent skill · NVIDIA
deepstream-generate-pipeline
Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).
What it needs
About 10k tokens when loaded.
What this skill does
DeepStream Pipeline Builder Generate ready-to-run gst-launch-1.0 pipelines for NVIDIA DeepStream SDK by collecting pipeline requirements through an interactive questionnaire, then assembling the pipeline using a standalone BM25 retrieval backend with structural metadata boosting (similarity search over 270+ verified pipelines, zero external dependencies). Prerequisites Python: 3.8+ (stdlib only — no pip packages required) DeepStream SDK: Installed at /opt/nvidia/deepstream/deepstream/ (for gst-inspect-1.0 validation and element verification) GStreamer: gst-launch-1.0 and gst-inspect-1.0 on PATH (installed with DeepStream) Platform: x86 dGPU (T4, A100, L40, RTX, etc.) or aarch64 — Jetson (Orin, Xavier, Nano) / SBSA (Grace, GH200) Usage Examples Supported Configurations Parameter Options --- --- Input Local video (.mp4/.h264/.h265), local image (.jpg/.png), RTSP stream, USB camera, test pattern Inference None, primary (nvinfer), primary+secondary, with preprocessor, Triton (nvinferserver) Tracker None, NvDCF, IOU, NvSORT, DeepSORT Sink Display (dGPU/Jetson), save (JPG/PNG/MP4/H264), RTSP out, fakesink Platform x86 dGPU (T4, A100, L40, RTX, etc.) or aarch64 — Jetson (Orin, Xavier, Nano) / SBSA (Grace, GH200) Extras Resize, rotate/flip, crop, color format conversion Scripts Script Purpose --- --- scripts/generatepipeline.py BM25 retrieval engine — scores and ranks pipelines from data/data.csv. Supports --format {json,compact,summary} (default json) scripts/validatepipeline.py 4-stage validator: syntax, elements, properties, live parse. Supports --format {json,summary} (default json) scripts/lintdata.py Data quality linter for the pipeline CSV (--fix to auto-repair) Workflow Step 1 — Collect Pipeline Requirements You MUST Read references/requirement-extraction.md before doing this step. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/deepstream-generate-pipeline