CE
Automation
cellular-senescence-agent
Cellular senescence analysis: marker scoring, SASP profiling, tissue aging assessment.
FreedomIntelligence/OpenClaw-Medical-SkillsView
Automation
IM
image-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
image-analysis.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/image-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Extract features and segment tissue images in spatial transcriptomics data.
- library_id = list(adata.uns['spatial'].keys())[0] img_dict = adata.uns['spatial'][library_id]['images'].
Source Doc
Excerpt From SKILL.md
High and low resolution images
hires = img_dict['hires'] lowres = img_dict['lowres']
print(f'Hires shape: {hires.shape}') print(f'Lowres shape: {lowres.shape}')
Get scale factors
scalef = adata.uns['spatial'][library_id]['scalefactors'] spot_diameter = scalef['spot_diameter_fullres'] hires_scale = scalef['tissue_hires_scalef']
## Squidpy's ImageContainer for organized image handling
img = sq.im.ImageContainer(adata.uns['spatial'][library_id]['images']['hires'])
print(img)
Use cases
- Use image-analysis in research workflows aligned with this subject area.
- Follow the upstream documentation for the full working procedure.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- spatial-data-io - Load spatial data with images
- spatial-visualization - Visualize images with expression
- spatial-domains - Use image features for domain detection
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Automation
consensus-sequences
consensus-sequences.
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Automation
docx
A user may ask you to create, edit, or analy.
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FI
Automation
filtering-best-practices
filtering-best-practices.
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