voyager-new-pipeline

Create New AI Pipeline

Create a YAML pipeline configuration for Axelera AI hardware using Voyager SDK

Use This Skill When / Not When

• Use when: the user explicitly wants a YAML pipeline configuration file only (detection, classification, segmentation, pose, cascade, parallel).
• Not when: they want it built, run, or validated -- route to voyager-launch.
• Not when: they want Python application code -- route to voyager-new-app.

Instructions

Follow these steps to create a new AI pipeline: $ARGUMENTS

Step 0: Data Source & Environment Selection

{{INCLUDE common/voyager-sdk-setup.md}}

Step 0.5: Axelera Voyager Project & Task Integration

{{INCLUDE common/voyager-task-integration.md}}

{{INCLUDE common/examples-catalog.md}}

Step 1: Pipeline Requirements Analysis

• Parse the pipeline type from arguments (detection, classification, segmentation, pose, cascade, parallel)
• If no arguments provided, ask user for:
  • Pipeline purpose (object detection, classification, segmentation, pose estimation, etc.)
  • Target model from model zoo or custom model
  • Input source type (camera, video file, image, RTSP stream)
  • Output requirements (display, file, metadata only)

Step 2: Model Selection

• Examples-first: check common/examples-catalog.md. If a shipped example matches the request, prefer running it (route complete/runnable requests to voyager-launch) rather than authoring a new pipeline.
• If the user wants a complete runnable package, validation, browser viewer, or output artifact, stop and route to voyager-launch.
• Query RAG and search local ax_models/zoo/, ax_models/reference/, pipeline-template/, and SDK tutorials for the exact task.
• Treat segmentation, depth, cascade, parallel, and LLM-adjacent requests as candidates until an SDK YAML and a Metis validation path are proven.
• Prefer existing model-zoo names and ONNX variants when the SDK lists them. Do not invent resolution variants or model names.