Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →mkurman/wandb
Weights & Biases — ML experiment tracking and visualization. Log metrics, hyperparameters, model checkpoints, and artifacts. Collaborative dashboards, sweep hyperparameter search, and model registry.
Passed 0 of 22 test cases with the skill, and 22 of 22 without it.
1 fixed20 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
What shell commands should I run in my terminal to install Weights & Biases into my Python environment and authenticate my machine before running experiments?
| Checked | Without skill | With skill |
|---|---|---|
| Uses 'uv pip install wandb' to install the package | Missed | Passed |
| Uses 'wandb login' to authenticate | Passed | Passed |
The instructions your model reads when the skill loads.
Weights & Biases (wandb) tracks ML experiments with rich visualizations, hyperparameter sweeps, dataset versioning, model registry, and collaborative dashboards. Industry standard for experiment tracking across ML teams.
uv pip install wandb
wandb login # authenticate with API key
import wandb
wandb.init(project="my_project", config={
"learning_rate": 0.001,
"batch_size": 32,
"architecture": "transformer",
})
for epoch in range(10):
loss = train_one_epoch()
wandb.log({"train_loss": loss, "val_loss": val_loss, "epoch": epoch})
wandb.finish()
sweep_config = {
"method": "bayes",
"metric": {"name": "val_loss", "goal": "minimize"},
"parameters": {"lr": {"min": 1e-5, "max": 1e-2}},
}
sweep_id = wandb.sweep(sweep_config, project="my_project")
wandb.agent(sweep_id, function=train_function, count=20)
test cases passed, vs 22 / 22 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.