Local AI assistant for the Axelera Voyager SDK and Metis AIPU — semantic RAG over the public Axelera sources + workflow skills, via MCP. No account, no cloud.
A local MCP server that gives an AI coding agent (Claude Code, or any MCP client) working knowledge of the Axelera Voyager SDK and Metis AIPU — semantic search over the public Axelera sources, plus project/task/document tracking. No account, no login, no token, no cloud.
- skills/ — 15 agent skills covering the Voyager workflow: build and launch a complete pipeline end to end, deploy/quantize a custom model, run inference on a source, benchmark FPS on hardware, debug a broken install or board, add a model to the zoo, scaffold an app, and more. They detect the environment (SDK present? Metis card visible?) and adapt, so they degrade to package-only output on a machine with no accelerator. - voyager-mcp/ — the MCP server the skills call: semantic search over the Axelera sources, plus project/task/document tracking.
Skill Use it for ------ voyager-launch one-shot: natural language → runnable pipeline, validated on Metis, with a result viewer voyager-deploy compile / quantize / calibrate a custom model for the AIPU voyager-run run inference.py on video, image, USB, RTSP voyager-bench measure FPS, latency, throughput voyager-debug diagnose pipelines, drivers, firmware, board detection voyager-add-model add a PyTorch/ONNX/timm/Ultralytics model to the zoo voyager-new-pipeline / voyager-new-app YAML pipeline design / standalone InferenceStream app voyager-list-models / voyager-help search the model zoo / explain the SDK voyager-agent, voyager-build, voyager-test, voyager-changelog, voyager-init routing, native builds, pytest, changelogs, workspace setup
From the project README.
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