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name: deepstream-generate-pipeline description: 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.). owner: NVIDIA CORPORATION service: deepstream version: 1.0.0 reviewed: 2026-04-27 license: CC-BY-4.0 AND Apache-2.0

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

# Fully specified — skips most questions
detect and track on 4 rtsp streams and display on jetson

# Partially specified — asks remaining questions
give me a pipeline to infer on an image

# Minimal — asks all 7 questions
build a pipeline

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/generate_pipeline.py | BM25 retrieval engine — scores and ranks pipelines from data/data.csv. Supports --format {json,compact,summary} (default json) | | scripts/validate_pipeline.py | 4-stage validator: syntax, elements, properties, live parse. Supports --format {json,summary} (default json) | | scripts/lint_data.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. It contains the query-inference table, compound-extraction examples, the full AskUserQuestion question bank (with the default-first ordering contract), the automatic-OSD and extras/flip-method rules, and the dynamic question-reduction examples that this step depends on. Apply them exactly.

Order of operations:

1. Infer everything you can from the query using the inference table in references/requirement-extraction.md. The goal is to identify which of the 7 parameters (input source, num sources, inference, tracker, sink, platform, extras) the user has already specified.
2. Ask the user about the unknowns via AskUserQuestion in a single call. Do not silently default tracker/sink/platform/extras — these are real choices the user should make explicitly (display vs save, no tracker vs NvDCF, x86 dGPU vs aarch64 Jetson/SBSA, etc.). Skip only the questions whose answer is already clear from the query.
3. Quote the inferred parameters back to the user in the lead-in to the question call so they can see what you already extracted. Example: "From your query I have: 3 mp4 videos, primary inference. Just need a few more details:"

Follow the inference table, question bank, and OSD/extras rules in references/requirement-extraction.md to decide which questions to ask and how to place transform elements, then proceed to Step 2.

Step 2 — Build the Natural Language Query

From the user's answers, construct a single descriptive query string. Follow this pattern:

Please provide a GStreamer pipeline that [operation] on [num_sources] [input_type] [input_detail] [tracker_detail] and [output_action] [platform_detail]

Examples of constructed queries:

| User Selections | Constructed Query | | --- | --- | | Local video, 1 source, Primary detector, No tracker, Display, dGPU | "Please provide a GStreamer pipeline that performs primary inference on a single mp4 video and displays the output" | | RTSP, 4 sources, Primary+Secondary, NvDCF, Save MP4, dGPU | "Please provide a GStreamer pipeline that performs primary and secondary inference with NvDCF tracker on 4 RTSP streams and saves output to MP4 file" | | Local video, 2 sources, Primary with preprocessor, IOU, Display, Jetson | "Please provide a GStreamer pipeline that performs preprocessing before primary inference with IOU tracker on 2 mp4 streams and displays the output on Jetson" | | Local image, 1 source, None, No tracker, Save file, dGPU, Rotate 90° cw | "Please provide a GStreamer pipeline that rotates a single jpg image 90° clockwise before processing and saves it to a file" | | Local video, 3 sources, Primary detector, NvDCF, Save MP4, dGPU, Rotate 180° | "Please provide a GStreamer pipeline that rotates 3 mp4 videos 180° before primary inference with NvDCF tracker and saves output to MP4 file" |

Step 3 — Run the Pipeline Generator Script

Execute the backend script with the constructed query and user parameters:

python3 <skill-path>/scripts/generate_pipeline.py \
  --query "<constructed_query>" \
  --source-type "<Local video file|Local image file|RTSP stream|USB camera|Test pattern>" \
  --num-sources <N> \
  --inference "<None|primary|primary+secondary|primary+preprocess|primary+secondary+preprocess|primary-triton|primary+secondary-triton>" \
  --tracker "<none|NvDCF|IOU|NvSORT|DeepSORT>" \
  --sink "<display|display-jetson|save-jpg|save-png|save-mp4|save-h264|rtsp-out|fakesink>" \
  --platform "<dGPU|Jetson|SBSA>" \
  --extras "<none|resize|rotate|crop|color-convert|osd>" \
  --format compact

Always pass --format compact. The compact mode returns only confidence + the top retrieved pipeline (~25 lines), instead of dumping all 10 retrievals as ~150 lines of JSON in the chat. The json mode (default for backward compat) is only useful when debugging the retriever directly. A summary mode (single human-readable line) also exists for non-Claude callers.

The script will (zero external dependencies — pure Python stdlib):

1. Load the pipeline dataset (270+ verified DeepStream pipelines)
2. Extract structural metadata from each pipeline (platform, source type, sink type, inference mode, tracker, stream count)
3. Score with BM25 (document-length-normalized) + domain-specific synonym expansion on both queries and documents
4. Apply structural boosting — results matching the user's platform/source/sink/inference get boosted, mismatches get penalized
5. Return the top-K results as JSON with a confidence field (high/medium/low) based on the top score
6. Claude uses these retrieved examples + the assembly rules below to construct the final pipeline

When confidence is low, rely more heavily on the assembly rules below rather than the retrieved examples.

Step 4 — Validate the Pipeline

Before presenting, run the validation script to catch syntax errors, unknown elements, and linking issues:

python3 <skill-path>/scripts/validate_pipeline.py "<assembled_pipeline>" --format summary

Always pass --format summary. Summary prints a single status line (e.g. valid · 11 elements · 0 warnings · live-parse skipped (multi-stream)), with errors/warnings indented underneath only if present. The default json mode emits ~40 lines of structured output and is only useful for programmatic callers.

The validator performs 4 checks:

1. Syntax check — unbalanced quotes, empty pipe segments, missing source/sink
2. Element check — verifies each element exists via gst-inspect-1.0
3. Property check — validates known properties for DeepStream elements
4. Live parse check — uses gst-launch-1.0 itself to construct the pipeline graph (with fakesrc/fakesink substituted), catching linking errors and pad mismatches. Automatically skipped for multi-stream pipelines (those with named pad refs like m.sink_0) since fakesrc cannot negotiate caps through named pads.

If validation fails ("valid": false), fix the errors and re-validate before presenting. Limit validation retries to a maximum of 2 attempts — if the pipeline still fails after 2 fixes, present it as-is (the remaining checks already cover syntax, element, property, and structural correctness). If there are only warnings, present the pipeline but mention the warnings to the user.

Step 5 — Present the Pipeline

5.1 — Output format (THE ONLY ACCEPTED FORM)

Your response must be exactly five blocks, in this order:

1. One-line status badge (validation + confidence)
2. Single bash code block containing the full gst-launch-1.0 -e … command with concrete absolute paths, on one line (no \ continuations, no shell variables, no shell wrapper)
3. Breakdown table grouped by stage
4. Suggestions bullet list
5. (only if pre-flight failed) a ⚠ line above the status badge stating which default path is missing

That is the ONLY accepted output shape for this step. The Section 5.3 template in references/output-format.md is the literal template — match it.

5.2 — Pre-flight check (run before composing the response)

Run one Bash ls over the default paths the pipeline will reference (sample video, PGIE config, tracker lib/config). The result tells you whether to mark the badge with ⚠ default path not found: <path> and bump the matching "Use your own …" suggestion to the top.

ls /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \
   /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt \
   2>&1

5.3 / 5.4 — Worked example & forbidden anti-patterns

You MUST Read references/output-format.md before composing this response. It contains the literal Section 5.3 template your output must match exactly, and the Section 5.4 gallery of forbidden output shapes (heredoc wrappers, shell-var indirection, \ line-continuations, stray "Run it" lines, Write-to-script). Mirror Section 5.3; never emit any Section 5.4 form.

5.5 — Self-check before sending the response

Before you emit your reply, mentally tick each box. If any check fails, rewrite the response.

• [ ] The pipeline is on exactly one line inside a single ```bash code block.
• [ ] The pipeline begins with gst-launch-1.0 -e and contains only literal absolute paths (e.g. /opt/nvidia/deepstream/...) — no $VAR, no ${VAR:-default}, no cat >, no EOF, no \ line continuations.
• [ ] The response does not contain any of: cat > /tmp/pipeline.sh, bash /tmp/pipeline.sh, <<'EOF', ${VAR:-.
• [ ] The response does not call the Write tool. (Save-to-file is offered as a suggestion bullet, not an action.)
• [ ] The breakdown table is grouped by stage (Source / Mux / Inference / Tracking / Composition / Render — adapt names to the pipeline's actual stages, e.g. add an Encode/Mux row for file sinks).
• [ ] The "Save it to a script?" line appears in the Suggestions list — never as a primary action.

5.6 — Pre-flight failure variant

If the Section 5.2 ls reported one or more missing d