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napari-image-viewer

View and annotate multi-dimensional microscopy images with napari layers.

298|27|Updated Feb 18, 2026
Search Tags:#visualization#microscopy#annotation#image viewer#bioimage analysis#napari
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill napari-image-viewer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: napari-image-viewer
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/cell-biology/napari-image-viewer
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill napari-image-viewer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires napari, numpy, scikit-image, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides an interactive, multi-dimensional viewer for scientific microscopy data, enabling efficient visualization, annotation, and quality-checking of complex image datasets.

Core Features & Use Cases

  • Interactive Visualization: Displays 2D, 3D, and 4D image data with multiple layer types (Image, Labels, Points, Shapes).
  • Annotation Tools: Supports real-time annotation for training data or region of interest definition.
  • Plugin Integration: Works seamlessly with popular bioimage analysis plugins like Cellpose and StarDist.
  • Headless Export: Allows for automated screenshot generation in non-GUI environments.
  • Use Case: Reviewing segmentation masks generated by Cellpose, overlaying multiple fluorescent channels, or annotating cell boundaries for machine learning models.

Quick Start

Open a napari viewer with a sample image and add it as an image layer.

Frequently Asked Questions about napari-image-viewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I visualize multi-dimensional microscopy data for bioimage analysis?▼

You can visualize multi-dimensional microscopy data using an interactive viewer that supports 2D, 3D, and 4D scientific images. It handles multiple layer types including Image, Labels, Points, and Shapes for complex bioimage analysis workflows.

Can I annotate cell boundaries and define regions of interest for machine learning models?▼

Yes, you can annotate cell boundaries and define regions of interest using real-time annotation tools. These features support the creation of training data and overlay multiple fluorescent channels for detailed inspection.

Does this image viewer work with Cellpose and StarDist segmentation plugins?▼

The viewer works seamlessly with popular bioimage analysis plugins like Cellpose and StarDist. You can review generated segmentation masks and integrate advanced analysis tools directly within the viewer environment.

Do I need numpy and scikit-image to view scientific images?▼

Yes, numpy and scikit-image are required dependencies for core functionality alongside napari. These libraries provide the necessary array manipulation and image processing capabilities for visualizing scientific images.

What is the best way to generate automated screenshots of image layers in a headless environment?▼

The best way to generate automated screenshots in non-GUI environments is using the headless export feature. This allows you to capture image layers and viewer states without launching the interactive interface.

Can I overlay multiple fluorescent channels in a single viewer window?▼

Yes, you can overlay multiple fluorescent channels by adding them as separate image layers within the same viewer window. This allows direct visual comparison and quality-checking of complex multi-channel microscopy datasets.

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