skills

Analyze PyTorch CUDA memory snapshots from torch.cuda.memory._dump_snapshot(), torch.cuda.memory._snapshot(), or memory_viz artifacts. Use when the user wants to understand GPU memory usage, top allocation sites, deallocation timing, OOMs, allocator fragmentation, inactive cached memory, memory leaks, retained tensors, or Python reference cycles in PyTorch workloads. Supports diffing two snapshots from the same run to pinpoint growing allocation sites.

Migrate C++/CUDA PyTorch extension code to use the PyTorch stable ABI. Use when the user wants to make their project ABI stable, migrate to stable ABI, or use torch::stable APIs.

A dummy skill that announces the arrival of agents. Use when the user asks about agents, the agent revolution, or wants to hype up agents.

Debug and fix torch.compile graph breaks to prioritize and eliminate graph-breaks, and optionally achieve fullgraph compilation

Analyze PyTorch profiler traces (.json/.json.gz). Use when the user wants to diagnose GPU performance issues, find slow kernels, identify idle time, analyze communication overhead, or debug training bottlenecks.