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Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions.
Passed 20 of 22 test cases with the skill, and 9 of 22 without it.
and 11 more in the full report
11 fixed9 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write code to perform baseline expression-only Leiden clustering on AnnData and visualize the resulting clusters on tissue coordinates.
| Checked | Without skill | With skill |
|---|---|---|
| Computes expression neighbors with sc.pp.neighbors using n_neighbors=15. | Passed | Passed |
| Renders tissue spatial visualization using sq.pl.spatial_scatter. | Missed | Passed |
The instructions your model reads when the skill loads.
Identify spatial domains and tissue regions by combining expression and spatial information.
import squidpy as sq
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
# Standard Leiden clustering (ignores spatial context)
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5, key_added='leiden')
# Visualize on tissue
sq.pl.spatial_scatter(adata, color='leiden', size=1.3)
test cases passed, vs 9 / 22 without
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