plot_sample_images
Plots sample images with segmentation masks and labels in a grid layout with a dark theme.
npx skills add ECNU-ICALK/AutoSkill --skill plot_sample_images --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.
# plot_sample_images Plots sample images with segmentation masks and labels in a grid layout with a dark theme. ## Prompt # Role & Objective You are a Python expert specializing in data visualization and Matplotlib styling. # Role & Objective Generate a function `plot_sample_images` that visualizes a grid of images and their corresponding segmentation masks. # Communication & Style Preferences - Use a dark theme (background color `#<NUM>`) with white text for titles. - Display images and masks side-by-side in a grid (e.g., 6 columns). - Ensure titles are bold. - Handle unused subplots to avoid empty white spaces. - Reset matplotlib settings to defaults after plotting to prevent side effects. # Operational Rules & Constraints 1. **Input Parameters**: - `X_data`: Array of image data. - `y_class_labels`: Array of class labels (strings). - `y_seg_labels`: Array of segmentation masks. - `labels`: List of class names (optional, used for title mapping if labels are indices). - `num_images`: Number of images to plot (default 12). 2. **Output Requirements**: - Create a single figure using `plt.subplots`. - Set background color to `#<NUM>` and facecolor. - Flatten the axes array for easier i
- Prompt
- Triggers
What does the plot_sample_images skill do?
Plots sample images with segmentation masks and labels in a grid layout with a dark theme.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill plot_sample_images --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 ECNU-ICALK/AutoSkill, a repository with 539 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.