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voyager-add-model

Add a custom model to the Voyager SDK model zoo for Axelera AI hardware. Use when the user wants to integrate a new PyTorch, ONNX, timm, or Ultralytics model.

Benchmark model performance on Axelera AI Metis hardware with the Voyager SDK. Use when the user wants to measure FPS, throughput, latency, or compare model variants. Use to quantify a working pipeline. Prefer voyager-debug when performance is a suspected fault or regression.

voyager-build

Build GStreamer operators, trackers, and C/C++ examples for Axelera AI hardware. Use when the user wants to compile native Voyager SDK components.

voyager-debug

Systematically debug and fix issues with Axelera AI Voyager SDK pipelines, models, inference, installation, or hardware. Use when the user brings an existing error, log, crash, accuracy issue, or regression, or when the Voyager SDK install is missing, broken, or outdated, the driver or firmware mismatch, or the Metis board is not detected or not working. Prefer voyager-launch when debugging is part of building a new end-to-end pipeline, and voyager-bench for pure performance measurement.

voyager-deploy

Compile and deploy a model pipeline for Axelera AI hardware using the Voyager SDK, especially custom or non-prebuilt models. Use when the user explicitly asks for compilation, quantization, or calibration, or wants to deploy or optimize a model for Axelera APU. For prebuilt model-zoo networks or complete runnable demos, prefer voyager-launch.

voyager-help

Get help with Voyager SDK for Axelera AI hardware. Use when the user asks questions about Voyager SDK, Axelera hardware, pipelines, InferenceStream API, or model zoo. Explanation-only answers; no code changes or pipeline runs.

voyager-init

Initialize Axelera Voyager MCP server integration for task management, knowledge base, and project tracking. Use when the user wants to set up Voyager for a new Voyager SDK workspace.

voyager-launch

Complete one-shot Voyager SDK launcher for Axelera AI Metis pipelines and demos. Use when the user asks to build, create, launch, run, verify, package, open a browser result viewer, or show results for a complete AI vision pipeline from a natural-language request (including when they want to debug or evaluate a pipeline you are building end-to-end), covering detection, tracking, pose, face, segmentation, depth estimation, video, USB camera, RTSP, app/demo, or output-video workflows. Prefer this over voyager-new-pipeline, voyager-run, voyager-deploy, voyager-bench, and voyager-new-app whenever the user wants an autonomous end-to-end runnable solution with RUN_HERE.sh, Metis execution, validation artifacts, logs, browser viewer, and evaluation output.

voyager-list-models

List and search models in the Voyager SDK model zoo for Axelera AI hardware. Use when the user wants to find available models by task, framework, or dataset.

voyager-new-app

Create or modify a standalone Python application using the Voyager SDK InferenceStream/create_inference_stream API for Axelera AI Metis hardware. Use when the user explicitly asks for app code, monitoring, analytics, REST API, custom UI, multi-stream logic, alerts, or business logic around an existing/validated pipeline. Do not use for generic complete pipeline launch, RUN_HERE.sh packaging, or hardware validation; use voyager-launch first for end-to-end runnable solutions.

voyager-new-pipeline

Create a YAML pipeline configuration for Axelera AI hardware using the Voyager SDK. Use when the user wants to design detection, classification, segmentation, pose, cascade, or parallel pipelines and explicitly wants YAML-only design. Do not use to build, run, or validate a runnable pipeline; prefer voyager-launch for end-to-end requests.

voyager-run

Execute inference.py for an already-selected Voyager SDK model or pipeline on Axelera AI Metis hardware with an explicit source such as video, image, USB, RTSP, dataset, or fakevideo. Use when the user provides or already has the model/pipeline and wants only a run/test/stream command. Do not create, package, validate, or evaluate a complete new solution from a natural-language request; use voyager-launch for that.

voyager-test

Run pytest suites for Voyager SDK components targeting Axelera AI hardware. Use when the user wants to execute unit tests, integration tests, or coverage reports. Not for testing a model on a video or camera; use voyager-run for that.

voyager-agent

General Axelera AI Voyager SDK and Metis hardware router for broad repository orientation, task planning, or multi-step work spanning several skills that is not a specific launch, run, deploy, debug, benchmark, app, test, build, or model-zoo task. Route explanation and architecture questions to voyager-help. Activate for any Axelera/Voyager request that does not clearly match a narrower Voyager skill. Prefer voyager-launch for complete runnable pipeline/demo requests.

voyager-changelog

Generate a Keep a Changelog fragment for the current PR in the Axelera Voyager / Voyager SDK development workflow. Provide a PR number or let the skill derive it from gh pr view.