scikit-image Otsu Segmentation Starter
SkillMediaUse this skill to generate a deterministic toy microscopy-like image, segment bright objects with Otsu thresholding, and summarize labeled regions.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the scikit-image Otsu Segmentation Starter skill
About this capability
A framework for discovering, compiling, and validating reusable skills for scientific agents.
What this skill tells your AI
The instructions your AI receives, as published by ma-compbio-lab/skillfoundry in skills/imaging-and-phenotype-analysis/skimage-otsu-segmentation-starter/SKILL.md and read by ahel’s review.
Use this skill to generate a deterministic toy microscopy-like image, segment bright objects with Otsu thresholding, and summarize labeled regions.
What it does
- Creates a synthetic grayscale image with three bright disk-shaped objects.
- Smooths the image, applies Otsu thresholding, removes tiny objects, and labels connected components.
- Returns compact JSON with threshold, object count, and per-object measurements.
When to use it
- You need a local imaging starter in an officially documented ecosystem.
- You want a minimal segmentation baseline before moving to heavier microscopy pipelines.
Example
slurm/envs/scientific-python/bin/python skills/imaging-and-phenotype-analysis/skimage-otsu-segmentation-starter/scripts/run_skimage_otsu_segmentation.py \
--out scratch/skimage/segmentation_summary.json
Verification
- Skill-local tests:
python3 -m unittest discover -s skills/imaging-and-phenotype-analysis/skimage-otsu-segmentation-starter/tests -p 'test_*.py' - Repository smoke:
python3 -m unittest tests.smoke.test_frontier_domain_skills -v
Signals
- GitHub stars
- 39
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
skimage-otsu-segmentation-starter- Source
- github.com/ma-compbio-lab/skillfoundry