metal-kernel
pytorchWrite Metal/MPS kernels for PyTorch operators. Use when adding MPS device support to operators, implementing Metal shaders, or porting CUDA kernels to Apple Silicon. Covers native_functions.yaml dispatch, host-side operators, and Metal kernel implementation.
torchforge-rl-training
davila7Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infrastructure from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
write-tests
meta-pytorchWrite failing tests from requirements. Invoke for each todo before /implement.
rfc-check
meta-pytorchDetermine if proposed changes require an RFC. Use when planning significant changes, before starting major work, or when asked whether an RFC is needed.
implement
meta-pytorchMake tests pass. Invoke after /write-tests produces failing tests.
pre-submit-pr
meta-pytorchValidate changes before submitting a pull request. Run comprehensive checks including lint, tests, alignment review, and RFC analysis. Use before creating a PR, when asked if code is ready for review, or before pushing for PR.
alignment-review
meta-pytorchReview code changes for bugs and alignment with OpenEnv principles and RFCs. Use when reviewing PRs, checking code before commit, or when asked to review changes. Implements two-tier review model.
simplify
meta-pytorchRefactor code after tests pass. The 'Refactor' phase of Red-Green-Refactor.
work-on-issue
meta-pytorchStart work on a GitHub issue. Extracts requirements, creates worktree, sets up TDD workflow.
mlx-dev
ettrickshepherdWrite correct, idiomatic Apple MLX code for Apple Silicon ML. Use when working with MLX arrays, neural networks, training loops, lazy evaluation, unified memory, mx.eval, mx.compile, Metal GPU, memory optimization, quantization, or Apple Silicon performance. Covers critical API differences from PyTorch/NumPy, array indexing gotchas (lists must be mx.array, slices create copies), NHWC format for Conv2d, __call__ not forward(), float64 CPU-only, mlx-lm integration, and debugging patterns.
mlx-dev
luqmannurhakimbazmanWrite correct, idiomatic Apple MLX code for Apple Silicon ML. Use when working with MLX arrays, neural networks, training loops, lazy evaluation, unified memory, mx.eval, mx.compile, Metal GPU, memory optimization, quantization, or Apple Silicon performance. Covers critical API differences from PyTorch/NumPy, array indexing gotchas (lists must be mx.array, slices create copies), NHWC format for Conv2d, __call__ not forward(), float64 CPU-only, mlx-lm integration, and debugging patterns.