Tensorboard

Solid wrapper around Google's TensorBoard for tracking ML experiments directly from Claude. Covers the essentials: logging scalars during training, visualizing model graphs, comparing hyperparameter runs, and using the embedding projector for high-dimensional data. The skill gives you working examples for both PyTorch and TensorFlow, which is helpful since the APIs differ. The hyperparameter comparison feature is genuinely useful when you're running multiple experiments and need to see which config actually worked. Main value is having the boilerplate ready to go instead of looking up the SummaryWriter API for the hundredth time. Best for anyone doing iterative model development who needs to see what's happening during training without cluttering up notebooks with matplotlib.