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Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Passed 21 of 22 test cases with the skill, and 19 of 22 without it.
2 fixed19 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am training a Keras model using model.fit() and want Weights & Biases to automatically track epoch metrics without writing manual logging loops. I am tempted to write a custom tf.keras.callbacks.Callback subclass with on_epoch_end hooks. What built-in callback class from W&B should be passed to model.fit?
| Checked | Without skill | With skill |
|---|---|---|
| WandbCallback from wandb.keras is instantiated | Missed | Passed |
| The WandbCallback instance is passed in the callbacks list argument of model.fit | Missed | Passed |
The instructions your model reads when the skill loads.
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal => bashweb_extract => web_fetch, plus web_search when discovery is neededsearch_files => grep_search and glob_searchbrowser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitlyWhen the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Use Weights & Biases (W&B) when you need to:
Users: 200,000+ ML practitioners | GitHub Stars: 10.5k+ | Integrations: 100+
# Install W&B
pip install wandb
# Login (creates API key)
wandb login
# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here
test cases passed, vs 19 / 22 without
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