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Lightweight WSI tile extraction and preprocessing.
Passed 22 of 22 test cases with the skill, and 8 of 22 without it.
and 19 more in the full report
14 fixed8 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to load one of the built-in TCGA sample slides (specifically prostate tissue) provided by the digital pathology Python processing tool and inspect its basic dimensions.
| Checked | Without skill | With skill |
|---|---|---|
| The response imports prostate_tissue from histolab.data. | Missed | Passed |
| The response initializes Slide using the sample file path. | Missed | Passed |
The instructions your model reads when the skill loads.
Histolab is a Python library for processing whole slide images (WSI) in digital pathology. It automates tissue detection, extracts informative tiles from gigapixel images, and prepares datasets for deep learning pipelines. The library handles multiple WSI formats, implements sophisticated tissue segmentation, and provides flexible tile extraction strategies.
Install OpenSlide system libraries first (OpenSlide download), then install histolab:
uv pip install histolab
For built-in TCGA sample slides via histolab.data, also install pooch:
uv pip install pooch
Histolab 0.7.0 (latest stable) supports Python 3.8–3.11 on Linux and macOS. Windows is not supported as of 0.7.0.
Basic workflow for extracting tiles from a whole slide image:
from histolab.slide import Slide
from histolab.tiler import RandomTiler
# Load slide
slide = Slide("slide.svs", processed_path="output/")
# Configure tiler
tiler = RandomTiler(
tile_size=(512, 512),
n_tiles=100,
level=0,
seed=42
)
test cases passed, vs 8 / 22 without
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