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Visualize datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction.
Passed 14 of 18 test cases with the skill, and 7 of 18 without it.
and 10 more in the full report
10 fixed7 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
After computing uniqueness on 'satellite_crops', I want to construct a view displaying the top 50 most unique items first. How should I set up the view sorting call?
| Checked | Without skill | With skill |
|---|---|---|
| calls set_view with sort_by set to uniqueness | Missed | Passed |
| sets reverse parameter to True to place highest anomaly scores first | Passed | Passed |
The instructions your model reads when the skill loads.
Visualize your dataset in 2D using deep learning embeddings and dimensionality reduction (UMAP/t-SNE). Explore clusters, find outliers, and color samples by any field.
Use this skill when:
@voxel51/brain plugin installed and enabledALWAYS follow these rules:
set_context(dataset_name="my-dataset")
Brain operators are delegated and require the app:
launch_app()
Wait 5-10 seconds for initialization.
# List all brain operators
list_operators(builtin_only=False)
test cases passed, vs 7 / 18 without
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