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This skill should be used when working with genomic interval data (BED files) for machine learning tasks.
Passed 20 of 22 test cases with the skill, and 5 of 22 without it.
and 22 more in the full report
15 fixed5 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to set up my Python environment for genomic interval embeddings with PyTorch and machine learning support. I use uv as my package manager rather than standard pip. What exact terminal command installs the genomic interval machine learning library along with its machine learning dependencies?
| Checked | Without skill | With skill |
|---|---|---|
| Executes uv pip install 'geniml[ml]' | Missed | Passed |
The instructions your model reads when the skill loads.
Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
Install geniml using uv:
uv pip install geniml
For ML dependencies (PyTorch, etc.):
uv pip install 'geniml[ml]'
Development version from GitHub:
uv pip install git+https://github.com/databio/geniml.git
Geniml provides five primary capabilities, each detailed in dedicated reference files:
Train unsupervised embeddings of genomic regions using word2vec-style learning.
Use for: Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.
Workflow:
test cases passed, vs 5 / 22 without
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