claude

burn-expert

Comprehensive Burn deep learning framework expert. Application development, training workflows, model import, backend extension, and framework contribution.

1 on GitHubv0.1.0MITupdated 9 months ago

Not in a marketplace yet, so it loads for one session with --plugin-dir.

Plugin burn-expert in johnzfitch/burn-plugin. Commands read from the repo's manifest — 1 month ago.

This plugin runs code on your machine — its hooks fire automatically on events like tool use and it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (9)

  • burn-app-dev

    This skill should be used when the user asks about "Burn tensors", "tensor operations", "Module derive", "burn config", "autodiff", "backward pass", "gradient", "record serialization", "model weights", or core Burn application development patterns.

  • burn-backends

    This skill should be used when the user asks about "Burn backend", "WGPU", "NdArray", "Candle", "LibTorch", "custom kernel", "CubeCL", "quantization", "WebAssembly", "no_std", or backend selection and extension.

  • burn-contrib

    This skill should be used when the user asks about "contributing to Burn", "adding an operation", "burn internals", "burn-tensor", "burn-autodiff", "ONNX converter", "NodeProcessor", or Burn framework development.

  • burn-debugger

    Diagnoses Burn deep learning errors including tensor shape mismatches, backend panics, autodiff issues, and training failures. Use when encountering runtime errors, compilation failures, or unexpected behavior in Burn code.

  • burn-onnx-surgeon

    Specializes in ONNX model import failures, unsupported operators, opset version mismatches, and dynamic shape issues. Use when burn-import fails or produces incorrect results.

  • burn-onnx

    This skill should be used when the user asks about "ONNX import", "PyTorch to Burn", "safetensors", "model conversion", "burn-import", "unsupported operator", "opset version", or importing external models into Burn.

  • burn-reviewer

    Reviews Burn deep learning code for idiomatic patterns, performance, backend portability, and best practices. Use when asked for code review, refactoring suggestions, or "is this idiomatic".

  • burn-router

    This skill should be used when the user mentions "burn", "tensor", "module", "backend", "training", "ONNX", "autodiff", "deep learning in Rust", or any Burn framework related query. Routes to appropriate domain skills and enforces evidence-seeking behavior.

  • burn-training

    This skill should be used when the user asks about "training loop", "Learner", "metrics", "dataset", "dataloader", "checkpointing", "optimizer", "learning rate scheduler", "custom training", or Burn training workflows.

Slash commands (4)

  • /burn-expert:burn-import

    Import ONNX, PyTorch, or Safetensors models into Burn

  • /burn-expert:burn-new

    Scaffold a new Burn deep learning project with selected backend

  • /burn-expert:burn-smoke

    Quick verification - format, check, and test Burn project

  • /burn-expert:burn-train

    Set up a Burn training loop with dataset, learner, and metrics

MCP servers (1)

  • burn-docs

    runs: llmx-mcp

Hooks (1)

  • PostToolUse

    bash ${CLAUDE_PLUGIN_ROOT}/hooks/scripts/post_edit_rustfmt.sh "$TOOL_INPUT_FILE_PATH"

View source on GitHub

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