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rHedBull AI Skills

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Browse published AI skills maintained by rHedBull. Compare skill descriptions and follow each source repository for setup instructions.

Author statistics

Indexed skills

8

Repository stars across indexed skills

0

Repository forks

0

Average repository stars per indexed skill

0

Repository stars may repeat across skills from the same repository.

8 skills

Browse skills

GitHub

hyperparam-sweep

rHedBull

Systematically search hyperparameter space. Use when tuning learning rate, batch size, or other hyperparameters.

GitHub

train-debug

rHedBull

Interactive diagnostic workflow for training problems. Use when training is failing, loss is stuck, gradients explode, NaN occurs, or convergence is poor.

GitHub

train

rHedBull

Execute a neural network training run with mandatory monitoring and best-practice defaults. Use when user wants to train a model, start training, or run a training job.

GitHub

experiment-plan

rHedBull

Design rigorous experiments before running them. Use when planning experiments, testing hypotheses, or designing ablation studies.

GitHub

monitor

rHedBull

View training progress in real-time. Use when checking on running training, viewing live metrics, or opening dashboards.

GitHub

run-validation

rHedBull

Run model validation on a dataset. Use when testing model performance, comparing checkpoints, or running final evaluation.

GitHub

interpret-curves

rHedBull

Analyze training logs and explain what's happening. Use when reviewing training runs, understanding loss curves, or diagnosing completed training.

GitHub

scaling-analysis

rHedBull

Run scaling experiments to understand model/data/compute relationships. Use when investigating scaling laws, compute-optimal training, or model size decisions.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.