napari-image-viewer
Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.
npx skills add BioTender-max/awesome-bio-agent-skills --skill napari-image-viewer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# napari — Multi-dimensional Image Viewer ## Overview napari is a fast, interactive multi-dimensional viewer for scientific data built on PyQt5 and VisPy. It displays NumPy arrays and zarr arrays as layered visualizations — Image layers for raw data, Labels layers for segmentation masks, Points layers for cell centroids, and Shapes layers for ROI annotations. napari integrates with scikit-image, Cellpose, and StarDist via plugins, making it the standard visualization and annotation tool in Python bioimage analysis pipelines. For headless environments (HPC, CI), napari supports offscreen rendering and `viewer.screenshot()` for automated figure generation. ## When to Use - Visually inspecting and quality-checking microscopy images and segmentation masks before quantitative analysis - Annotating training data for deep learning segmentation models (Cellpose, StarDist) - Overlaying multiple image channels (DAPI, GFP, mCherry) with independent contrast and colormap control - Reviewing 3D z-stacks and 4D time-lapse experiments with slider-based navigation - Exporting annotated screenshots or label masks from GUI for publication figures - Running plugin-based analysis (Cellpose napari plug
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Image Layer — Display Raw Images
- Module 2: Labels Layer — Visualize Segmentation Masks
- Module 3: Points Layer — Mark Cell Centroids
- Module 4: Shapes Layer — Draw ROIs and Annotations
- Module 5: 3D and Time-lapse Visualization
- Module 6: Headless Screenshot Export
- Key Parameters
- Common Workflows
- Workflow 1: Review Cellpose Segmentation Quality
Install with all backends pip install "napari[all]" Or minimal install pip install napari pyqt5 Verify python -c "import napari; print(napari.__version__)" Install useful plugins pip install napari-cellpose napari-animation
What does the napari-image-viewer skill do?
Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill napari-image-viewer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.