How do I install the Medical Imaging Workflows skill?

Run npx skillmds@latest add yuukias/medical-imaging-workflows in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Medical Imaging Workflows safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Medical Imaging Workflows?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Medical Imaging Workflows free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Medical Imaging Workflows?

YuukiAS (@yuukias) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Medical Imaging Workflows by yuukias

Name: Medical Imaging Workflows
Author: YuukiAS

File contents

medical-imaging-workflows

Trigger Boundary

Medical imaging workflows for CMR, DICOM/NIfTI, classical features, deep learning, and pathology imaging.

Use this aggregate Codex App skill when the task matches one of the source workflows below.

Source Workflows

cardiac-mri: Use for cardiac MRI / CMR domain knowledge, cine SAX/LAX, ED/ES timing, LV/RV function, myocardial strain, tagged MRI, feature tracking, and cardiac phenotype validation independent of any single project. Reference: _src/cmr/source.md
pydicom: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications. Reference: _src/dicom/source.md
medical-imaging-classical-features: Use when enforcing reproducible medical-imaging preprocessing, physical-space geometry, classical registration baselines, radiomics protocols, or DICOM SEG/SR provenance. Reference: _src/classic/source.md
medical-imaging-deep-learning: Use for medical-imaging deep learning tasks involving segmentation, MONAI/nnU-Net baselines, registration or warping, temporal/video imaging, missing-modality fusion, proposal/cascade/refinement models, external method adaptation, and validation evidence gates. Reference: _src/dl/source.md
pathml: Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler. Reference: _src/pathml/source.md

Workflow

Choose the source workflow whose trigger boundary best matches the user request.
Read that source workflow's source.md before acting.
Load only the needed files under that workflow's copied references, scripts, assets, or evals.
Follow the source workflow unless the current project gives stricter instructions.

---name: medical-imaging-workflowsdescription: Medical imaging workflows for CMR, DICOM/NIfTI, classical features, deep learning, and pathology imaging.---# medical-imaging-workflows## Trigger BoundaryMedical imaging workflows for CMR, DICOM/NIfTI, classical features, deep learning, and pathology imaging.Use this aggregate Codex App skill when the task matches one of the source workflows below.## Source Workflows- `cardiac-mri`: Use for cardiac MRI / CMR domain knowledge, cine SAX/LAX, ED/ES timing, LV/RV function, myocardial strain, tagged MRI, feature tracking, and cardiac phenotype validation independent of any single project. Reference: `_src/cmr/source.md`- `pydicom`: Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications. Reference: `_src/dicom/source.md`- `medical-imaging-classical-features`: Use when enforcing reproducible medical-imaging preprocessing, physical-space geometry, classical registration baselines, radiomics protocols, or DICOM SEG/SR provenance. Reference: `_src/classic/source.md`- `medical-imaging-deep-learning`: Use for medical-imaging deep learning tasks involving segmentation, MONAI/nnU-Net baselines, registration or warping, temporal/video imaging, missing-modality fusion, proposal/cascade/refinement models, external method adaptation, and validation evidence gates. Reference: `_src/dl/source.md`- `pathml`: Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler. Reference: `_src/pathml/source.md`## Workflow1. Choose the source workflow whose trigger boundary best matches the user request.2. Read that source workflow's `source.md` before acting.3. Load only the needed files under that workflow's copied references, scripts, assets, or evals.4. Follow the source workflow unless the current project gives stricter instructions.

Frequently asked questions

Medical Imaging Workflows