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+---
+name: matplotlib
+description: "Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures."
+---
+
+# Matplotlib
+
+## Overview
+
+Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.
+
+## When to Use This Skill
+
+This skill should be used when:
+- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
+- Generating scientific or statistical visualizations
+- Customizing plot appearance (colors, styles, labels, legends)
+- Creating multi-panel figures with subplots
+- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
+- Building interactive plots or animations
+- Working with 3D visualizations
+- Integrating plots into Jupyter notebooks or GUI applications
+
+## Core Concepts
+
+### The Matplotlib Hierarchy
+
+Matplotlib uses a hierarchical structure of objects:
+
+1. **Figure** - The top-level container for all plot elements
+2. **Axes** - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
+3. **Artist** - Everything visible on the figure (lines, text, ticks, etc.)
+4. **Axis** - The number line objects (x-axis, y-axis) that handle ticks and labels
+
+### Two Interfaces
+
+**1. pyplot Interface (Implicit, MATLAB-style)**
+```python
+import matplotlib.pyplot as plt
+
+plt.plot([1, 2, 3, 4])
+plt.ylabel('some numbers')
+plt.show()
+```
+- Convenient for quick, simple plots
+- Maintains state automatically
+- Good for interactive work and simple scripts
+
+**2. Object-Oriented Interface (Explicit)**
+```python
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.plot([1, 2, 3, 4])
+ax.set_ylabel('some numbers')
+plt.show()
+```
+- **Recommended for most use cases**
+- More explicit control over figure and axes
+- Better for complex figures with multiple subplots
+- Easier to maintain and debug
+
+## Common Workflows
+
+### 1. Basic Plot Creation
+
+**Single plot workflow:**
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Create figure and axes (OO interface - RECOMMENDED)
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Generate and plot data
+x = np.linspace(0, 2*np.pi, 100)
+ax.plot(x, np.sin(x), label='sin(x)')
+ax.plot(x, np.cos(x), label='cos(x)')
+
+# Customize
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+ax.set_title('Trigonometric Functions')
+ax.legend()
+ax.grid(True, alpha=0.3)
+
+# Save and/or display
+plt.savefig('plot.png', dpi=300, bbox_inches='tight')
+plt.show()
+```
+
+### 2. Multiple Subplots
+
+**Creating subplot layouts:**
+```python
+# Method 1: Regular grid
+fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+axes[0, 0].plot(x, y1)
+axes[0, 1].scatter(x, y2)
+axes[1, 0].bar(categories, values)
+axes[1, 1].hist(data, bins=30)
+
+# Method 2: Mosaic layout (more flexible)
+fig, axes = plt.subplot_mosaic([['left', 'right_top'],
+                                 ['left', 'right_bottom']],
+                                figsize=(10, 8))
+axes['left'].plot(x, y)
+axes['right_top'].scatter(x, y)
+axes['right_bottom'].hist(data)
+
+# Method 3: GridSpec (maximum control)
+from matplotlib.gridspec import GridSpec
+fig = plt.figure(figsize=(12, 8))
+gs = GridSpec(3, 3, figure=fig)
+ax1 = fig.add_subplot(gs[0, :])  # Top row, all columns
+ax2 = fig.add_subplot(gs[1:, 0])  # Bottom two rows, first column
+ax3 = fig.add_subplot(gs[1:, 1:])  # Bottom two rows, last two columns
+```
+
+### 3. Plot Types and Use Cases
+
+**Line plots** - Time series, continuous data, trends
+```python
+ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
+```
+
+**Scatter plots** - Relationships between variables, correlations
+```python
+ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
+```
+
+**Bar charts** - Categorical comparisons
+```python
+ax.bar(categories, values, color='steelblue', edgecolor='black')
+# For horizontal bars:
+ax.barh(categories, values)
+```
+
+**Histograms** - Distributions
+```python
+ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
+```
+
+**Heatmaps** - Matrix data, correlations
+```python
+im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
+plt.colorbar(im, ax=ax)
+```
+
+**Contour plots** - 3D data on 2D plane
+```python
+contour = ax.contour(X, Y, Z, levels=10)
+ax.clabel(contour, inline=True, fontsize=8)
+```
+
+**Box plots** - Statistical distributions
+```python
+ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
+```
+
+**Violin plots** - Distribution densities
+```python
+ax.violinplot([data1, data2, data3], positions=[1, 2, 3])
+```
+
+For comprehensive plot type examples and variations, refer to `references/plot_types.md`.
+
+### 4. Styling and Customization
+
+**Color specification methods:**
+- Named colors: `'red'`, `'blue'`, `'steelblue'`
+- Hex codes: `'#FF5733'`
+- RGB tuples: `(0.1, 0.2, 0.3)`
+- Colormaps: `cmap='viridis'`, `cmap='plasma'`, `cmap='coolwarm'`
+
+**Using style sheets:**
+```python
+plt.style.use('seaborn-v0_8-darkgrid')  # Apply predefined style
+# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
+print(plt.style.available)  # List all available styles
+```
+
+**Customizing with rcParams:**
+```python
+plt.rcParams['font.size'] = 12
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['axes.titlesize'] = 16
+plt.rcParams['xtick.labelsize'] = 10
+plt.rcParams['ytick.labelsize'] = 10
+plt.rcParams['legend.fontsize'] = 12
+plt.rcParams['figure.titlesize'] = 18
+```
+
+**Text and annotations:**
+```python
+ax.text(x, y, 'annotation', fontsize=12, ha='center')
+ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
+            arrowprops=dict(arrowstyle='->', color='red'))
+```
+
+For detailed styling options and colormap guidelines, see `references/styling_guide.md`.
+
+### 5. Saving Figures
+
+**Export to various formats:**
+```python
+# High-resolution PNG for presentations/papers
+plt.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')
+
+# Vector format for publications (scalable)
+plt.savefig('figure.pdf', bbox_inches='tight')
+plt.savefig('figure.svg', bbox_inches='tight')
+
+# Transparent background
+plt.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True)
+```
+
+**Important parameters:**
+- `dpi`: Resolution (300 for publications, 150 for web, 72 for screen)
+- `bbox_inches='tight'`: Removes excess whitespace
+- `facecolor='white'`: Ensures white background (useful for transparent themes)
+- `transparent=True`: Transparent background
+
+### 6. Working with 3D Plots
+
+```python
+from mpl_toolkits.mplot3d import Axes3D
+
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+
+# Surface plot
+ax.plot_surface(X, Y, Z, cmap='viridis')
+
+# 3D scatter
+ax.scatter(x, y, z, c=colors, marker='o')
+
+# 3D line plot
+ax.plot(x, y, z, linewidth=2)
+
+# Labels
+ax.set_xlabel('X Label')
+ax.set_ylabel('Y Label')
+ax.set_zlabel('Z Label')
+```
+
+## Best Practices
+
+### 1. Interface Selection
+- **Use the object-oriented interface** (fig, ax = plt.subplots()) for production code
+- Reserve pyplot interface for quick interactive exploration only
+- Always create figures explicitly rather than relying on implicit state
+
+### 2. Figure Size and DPI
+- Set figsize at creation: `fig, ax = plt.subplots(figsize=(10, 6))`
+- Use appropriate DPI for output medium:
+  - Screen/notebook: 72-100 dpi
+  - Web: 150 dpi
+  - Print/publications: 300 dpi
+
+### 3. Layout Management
+- Use `constrained_layout=True` or `tight_layout()` to prevent overlapping elements
+- `fig, ax = plt.subplots(constrained_layout=True)` is recommended for automatic spacing
+
+### 4. Colormap Selection
+- **Sequential** (viridis, plasma, inferno): Ordered data with consistent progression
+- **Diverging** (coolwarm, RdBu): Data with meaningful center point (e.g., zero)
+- **Qualitative** (tab10, Set3): Categorical/nominal data
+- Avoid rainbow colormaps (jet) - they are not perceptually uniform
+
+### 5. Accessibility
+- Use colorblind-friendly colormaps (viridis, cividis)
+- Add patterns/hatching for bar charts in addition to colors
+- Ensure sufficient contrast between elements
+- Include descriptive labels and legends
+
+### 6. Performance
+- For large datasets, use `rasterized=True` in plot calls to reduce file size
+- Use appropriate data reduction before plotting (e.g., downsample dense time series)
+- For animations, use blitting for better performance
+
+### 7. Code Organization
+```python
+# Good practice: Clear structure
+def create_analysis_plot(data, title):
+    """Create standardized analysis plot."""
+    fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    # Plot data
+    ax.plot(data['x'], data['y'], linewidth=2)
+
+    # Customize
+    ax.set_xlabel('X Axis Label', fontsize=12)
+    ax.set_ylabel('Y Axis Label', fontsize=12)
+    ax.set_title(title, fontsize=14, fontweight='bold')
+    ax.grid(True, alpha=0.3)
+
+    return fig, ax
+
+# Use the function
+fig, ax = create_analysis_plot(my_data, 'My Analysis')
+plt.savefig('analysis.png', dpi=300, bbox_inches='tight')
+```
+
+## Quick Reference Scripts
+
+This skill includes helper scripts in the `scripts/` directory:
+
+### `plot_template.py`
+Template script demonstrating various plot types with best practices. Use this as a starting point for creating new visualizations.
+
+**Usage:**
+```bash
+python scripts/plot_template.py
+```
+
+### `style_configurator.py`
+Interactive utility to configure matplotlib style preferences and generate custom style sheets.
+
+**Usage:**
+```bash
+python scripts/style_configurator.py
+```
+
+## Detailed References
+
+For comprehensive information, consult the reference documents:
+
+- **`references/plot_types.md`** - Complete catalog of plot types with code examples and use cases
+- **`references/styling_guide.md`** - Detailed styling options, colormaps, and customization
+- **`references/api_reference.md`** - Core classes and methods reference
+- **`references/common_issues.md`** - Troubleshooting guide for common problems
+
+## Integration with Other Tools
+
+Matplotlib integrates well with:
+- **NumPy/Pandas** - Direct plotting from arrays and DataFrames
+- **Seaborn** - High-level statistical visualizations built on matplotlib
+- **Jupyter** - Interactive plotting with `%matplotlib inline` or `%matplotlib widget`
+- **GUI frameworks** - Embedding in Tkinter, Qt, wxPython applications
+
+## Common Gotchas
+
+1. **Overlapping elements**: Use `constrained_layout=True` or `tight_layout()`
+2. **State confusion**: Use OO interface to avoid pyplot state machine issues
+3. **Memory issues with many figures**: Close figures explicitly with `plt.close(fig)`
+4. **Font warnings**: Install fonts or suppress warnings with `plt.rcParams['font.sans-serif']`
+5. **DPI confusion**: Remember that figsize is in inches, not pixels: `pixels = dpi * inches`
+
+## Additional Resources
+
+- Official documentation: https://matplotlib.org/
+- Gallery: https://matplotlib.org/stable/gallery/index.html
+- Cheatsheets: https://matplotlib.org/cheatsheets/
+- Tutorials: https://matplotlib.org/stable/tutorials/index.html
--- a/skills/matplotlib/references/api_reference.md
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+# Matplotlib API Reference
+
+This document provides a quick reference for the most commonly used matplotlib classes and methods.
+
+## Core Classes
+
+### Figure
+
+The top-level container for all plot elements.
+
+**Creation:**
+```python
+fig = plt.figure(figsize=(10, 6), dpi=100, facecolor='white')
+fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))
+fig, axes = plt.subplots(2, 2, figsize=(12, 10))
+```
+
+**Key Methods:**
+- `fig.add_subplot(nrows, ncols, index)` - Add a subplot
+- `fig.add_axes([left, bottom, width, height])` - Add axes at specific position
+- `fig.savefig(filename, dpi=300, bbox_inches='tight')` - Save figure
+- `fig.tight_layout()` - Adjust spacing to prevent overlaps
+- `fig.suptitle(title)` - Set figure title
+- `fig.legend()` - Create figure-level legend
+- `fig.colorbar(mappable)` - Add colorbar to figure
+- `plt.close(fig)` - Close figure to free memory
+
+**Key Attributes:**
+- `fig.axes` - List of all axes in the figure
+- `fig.dpi` - Resolution in dots per inch
+- `fig.figsize` - Figure dimensions in inches (width, height)
+
+### Axes
+
+The actual plotting area where data is visualized.
+
+**Creation:**
+```python
+fig, ax = plt.subplots()  # Single axes
+ax = fig.add_subplot(111)  # Alternative method
+```
+
+**Plotting Methods:**
+
+**Line plots:**
+- `ax.plot(x, y, **kwargs)` - Line plot
+- `ax.step(x, y, where='pre'/'mid'/'post')` - Step plot
+- `ax.errorbar(x, y, yerr, xerr)` - Error bars
+
+**Scatter plots:**
+- `ax.scatter(x, y, s=size, c=color, marker='o', alpha=0.5)` - Scatter plot
+
+**Bar charts:**
+- `ax.bar(x, height, width=0.8, align='center')` - Vertical bar chart
+- `ax.barh(y, width)` - Horizontal bar chart
+
+**Statistical plots:**
+- `ax.hist(data, bins=10, density=False)` - Histogram
+- `ax.boxplot(data, labels=None)` - Box plot
+- `ax.violinplot(data)` - Violin plot
+
+**2D plots:**
+- `ax.imshow(array, cmap='viridis', aspect='auto')` - Display image/matrix
+- `ax.contour(X, Y, Z, levels=10)` - Contour lines
+- `ax.contourf(X, Y, Z, levels=10)` - Filled contours
+- `ax.pcolormesh(X, Y, Z)` - Pseudocolor plot
+
+**Filling:**
+- `ax.fill_between(x, y1, y2, alpha=0.3)` - Fill between curves
+- `ax.fill_betweenx(y, x1, x2)` - Fill between vertical curves
+
+**Text and annotations:**
+- `ax.text(x, y, text, fontsize=12)` - Add text
+- `ax.annotate(text, xy=(x, y), xytext=(x2, y2), arrowprops={})` - Annotate with arrow
+
+**Customization Methods:**
+
+**Labels and titles:**
+- `ax.set_xlabel(label, fontsize=12)` - Set x-axis label
+- `ax.set_ylabel(label, fontsize=12)` - Set y-axis label
+- `ax.set_title(title, fontsize=14)` - Set axes title
+
+**Limits and scales:**
+- `ax.set_xlim(left, right)` - Set x-axis limits
+- `ax.set_ylim(bottom, top)` - Set y-axis limits
+- `ax.set_xscale('linear'/'log'/'symlog')` - Set x-axis scale
+- `ax.set_yscale('linear'/'log'/'symlog')` - Set y-axis scale
+
+**Ticks:**
+- `ax.set_xticks(positions)` - Set x-tick positions
+- `ax.set_xticklabels(labels)` - Set x-tick labels
+- `ax.tick_params(axis='both', labelsize=10)` - Customize tick appearance
+
+**Grid and spines:**
+- `ax.grid(True, alpha=0.3, linestyle='--')` - Add grid
+- `ax.spines['top'].set_visible(False)` - Hide top spine
+- `ax.spines['right'].set_visible(False)` - Hide right spine
+
+**Legend:**
+- `ax.legend(loc='best', fontsize=10, frameon=True)` - Add legend
+- `ax.legend(handles, labels)` - Custom legend
+
+**Aspect and layout:**
+- `ax.set_aspect('equal'/'auto'/ratio)` - Set aspect ratio
+- `ax.invert_xaxis()` - Invert x-axis
+- `ax.invert_yaxis()` - Invert y-axis
+
+### pyplot Module
+
+High-level interface for quick plotting.
+
+**Figure creation:**
+- `plt.figure()` - Create new figure
+- `plt.subplots()` - Create figure and axes
+- `plt.subplot()` - Add subplot to current figure
+
+**Plotting (uses current axes):**
+- `plt.plot()` - Line plot
+- `plt.scatter()` - Scatter plot
+- `plt.bar()` - Bar chart
+- `plt.hist()` - Histogram
+- (All axes methods available)
+
+**Display and save:**
+- `plt.show()` - Display figure
+- `plt.savefig()` - Save figure
+- `plt.close()` - Close figure
+
+**Style:**
+- `plt.style.use(style_name)` - Apply style sheet
+- `plt.style.available` - List available styles
+
+**State management:**
+- `plt.gca()` - Get current axes
+- `plt.gcf()` - Get current figure
+- `plt.sca(ax)` - Set current axes
+- `plt.clf()` - Clear current figure
+- `plt.cla()` - Clear current axes
+
+## Line and Marker Styles
+
+### Line Styles
+- `'-'` or `'solid'` - Solid line
+- `'--'` or `'dashed'` - Dashed line
+- `'-.'` or `'dashdot'` - Dash-dot line
+- `':'` or `'dotted'` - Dotted line
+- `''` or `' '` or `'None'` - No line
+
+### Marker Styles
+- `'.'` - Point marker
+- `'o'` - Circle marker
+- `'v'`, `'^'`, `'<'`, `'>'` - Triangle markers
+- `'s'` - Square marker
+- `'p'` - Pentagon marker
+- `'*'` - Star marker
+- `'h'`, `'H'` - Hexagon markers
+- `'+'` - Plus marker
+- `'x'` - X marker
+- `'D'`, `'d'` - Diamond markers
+
+### Color Specifications
+
+**Single character shortcuts:**
+- `'b'` - Blue
+- `'g'` - Green
+- `'r'` - Red
+- `'c'` - Cyan
+- `'m'` - Magenta
+- `'y'` - Yellow
+- `'k'` - Black
+- `'w'` - White
+
+**Named colors:**
+- `'steelblue'`, `'coral'`, `'teal'`, etc.
+- See full list: https://matplotlib.org/stable/gallery/color/named_colors.html
+
+**Other formats:**
+- Hex: `'#FF5733'`
+- RGB tuple: `(0.1, 0.2, 0.3)`
+- RGBA tuple: `(0.1, 0.2, 0.3, 0.5)`
+
+## Common Parameters
+
+### Plot Function Parameters
+
+```python
+ax.plot(x, y,
+    color='blue',           # Line color
+    linewidth=2,            # Line width
+    linestyle='--',         # Line style
+    marker='o',             # Marker style
+    markersize=8,           # Marker size
+    markerfacecolor='red',  # Marker fill color
+    markeredgecolor='black',# Marker edge color
+    markeredgewidth=1,      # Marker edge width
+    alpha=0.7,              # Transparency (0-1)
+    label='data',           # Legend label
+    zorder=2,               # Drawing order
+    rasterized=True         # Rasterize for smaller file size
+)
+```
+
+### Scatter Function Parameters
+
+```python
+ax.scatter(x, y,
+    s=50,                   # Size (scalar or array)
+    c='blue',               # Color (scalar, array, or sequence)
+    marker='o',             # Marker style
+    cmap='viridis',         # Colormap (if c is numeric)
+    alpha=0.5,              # Transparency
+    edgecolors='black',     # Edge color
+    linewidths=1,           # Edge width
+    vmin=0, vmax=1,         # Color scale limits
+    label='data'            # Legend label
+)
+```
+
+### Text Parameters
+
+```python
+ax.text(x, y, text,
+    fontsize=12,            # Font size
+    fontweight='normal',    # 'normal', 'bold', 'heavy', 'light'
+    fontstyle='normal',     # 'normal', 'italic', 'oblique'
+    fontfamily='sans-serif',# Font family
+    color='black',          # Text color
+    alpha=1.0,              # Transparency
+    ha='center',            # Horizontal alignment: 'left', 'center', 'right'
+    va='center',            # Vertical alignment: 'top', 'center', 'bottom', 'baseline'
+    rotation=0,             # Rotation angle in degrees
+    bbox=dict(              # Background box
+        facecolor='white',
+        edgecolor='black',
+        boxstyle='round'
+    )
+)
+```
+
+## rcParams Configuration
+
+Common rcParams settings for global customization:
+
+```python
+# Font settings
+plt.rcParams['font.family'] = 'sans-serif'
+plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
+plt.rcParams['font.size'] = 12
+
+# Figure settings
+plt.rcParams['figure.figsize'] = (10, 6)
+plt.rcParams['figure.dpi'] = 100
+plt.rcParams['figure.facecolor'] = 'white'
+plt.rcParams['savefig.dpi'] = 300
+plt.rcParams['savefig.bbox'] = 'tight'
+
+# Axes settings
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['axes.titlesize'] = 16
+plt.rcParams['axes.grid'] = True
+plt.rcParams['axes.grid.alpha'] = 0.3
+
+# Line settings
+plt.rcParams['lines.linewidth'] = 2
+plt.rcParams['lines.markersize'] = 8
+
+# Tick settings
+plt.rcParams['xtick.labelsize'] = 10
+plt.rcParams['ytick.labelsize'] = 10
+plt.rcParams['xtick.direction'] = 'in'  # 'in', 'out', 'inout'
+plt.rcParams['ytick.direction'] = 'in'
+
+# Legend settings
+plt.rcParams['legend.fontsize'] = 12
+plt.rcParams['legend.frameon'] = True
+plt.rcParams['legend.framealpha'] = 0.8
+
+# Grid settings
+plt.rcParams['grid.alpha'] = 0.3
+plt.rcParams['grid.linestyle'] = '--'
+```
+
+## GridSpec for Complex Layouts
+
+```python
+from matplotlib.gridspec import GridSpec
+
+fig = plt.figure(figsize=(12, 8))
+gs = GridSpec(3, 3, figure=fig, hspace=0.3, wspace=0.3)
+
+# Span multiple cells
+ax1 = fig.add_subplot(gs[0, :])      # Top row, all columns
+ax2 = fig.add_subplot(gs[1:, 0])     # Bottom two rows, first column
+ax3 = fig.add_subplot(gs[1, 1:])     # Middle row, last two columns
+ax4 = fig.add_subplot(gs[2, 1])      # Bottom row, middle column
+ax5 = fig.add_subplot(gs[2, 2])      # Bottom row, right column
+```
+
+## 3D Plotting
+
+```python
+from mpl_toolkits.mplot3d import Axes3D
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+# Plot types
+ax.plot(x, y, z)                    # 3D line
+ax.scatter(x, y, z)                 # 3D scatter
+ax.plot_surface(X, Y, Z)            # 3D surface
+ax.plot_wireframe(X, Y, Z)          # 3D wireframe
+ax.contour(X, Y, Z)                 # 3D contour
+ax.bar3d(x, y, z, dx, dy, dz)       # 3D bar
+
+# Customization
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.view_init(elev=30, azim=45)      # Set viewing angle
+```
+
+## Animation
+
+```python
+from matplotlib.animation import FuncAnimation
+
+fig, ax = plt.subplots()
+line, = ax.plot([], [])
+
+def init():
+    ax.set_xlim(0, 2*np.pi)
+    ax.set_ylim(-1, 1)
+    return line,
+
+def update(frame):
+    x = np.linspace(0, 2*np.pi, 100)
+    y = np.sin(x + frame/10)
+    line.set_data(x, y)
+    return line,
+
+anim = FuncAnimation(fig, update, init_func=init,
+                     frames=100, interval=50, blit=True)
+
+# Save animation
+anim.save('animation.gif', writer='pillow', fps=20)
+anim.save('animation.mp4', writer='ffmpeg', fps=20)
+```
+
+## Image Operations
+
+```python
+# Read and display image
+img = plt.imread('image.png')
+ax.imshow(img)
+
+# Display matrix as image
+ax.imshow(matrix, cmap='viridis', aspect='auto',
+          interpolation='nearest', origin='lower')
+
+# Colorbar
+cbar = plt.colorbar(im, ax=ax)
+cbar.set_label('Values')
+
+# Image extent (set coordinates)
+ax.imshow(img, extent=[x_min, x_max, y_min, y_max])
+```
+
+## Event Handling
+
+```python
+# Mouse click event
+def on_click(event):
+    if event.inaxes:
+        print(f'Clicked at x={event.xdata:.2f}, y={event.ydata:.2f}')
+
+fig.canvas.mpl_connect('button_press_event', on_click)
+
+# Key press event
+def on_key(event):
+    print(f'Key pressed: {event.key}')
+
+fig.canvas.mpl_connect('key_press_event', on_key)
+```
+
+## Useful Utilities
+
+```python
+# Get current axis limits
+xlims = ax.get_xlim()
+ylims = ax.get_ylim()
+
+# Set equal aspect ratio
+ax.set_aspect('equal', adjustable='box')
+
+# Share axes between subplots
+fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
+
+# Twin axes (two y-axes)
+ax2 = ax1.twinx()
+
+# Remove tick labels
+ax.set_xticklabels([])
+ax.set_yticklabels([])
+
+# Scientific notation
+ax.ticklabel_format(style='scientific', axis='y', scilimits=(0,0))
+
+# Date formatting
+import matplotlib.dates as mdates
+ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
+```
--- a/skills/matplotlib/references/common_issues.md
+++ b/skills/matplotlib/references/common_issues.md
@@ -0,0 +1,563 @@
+# Matplotlib Common Issues and Solutions
+
+Troubleshooting guide for frequently encountered matplotlib problems.
+
+## Display and Backend Issues
+
+### Issue: Plots Not Showing
+
+**Problem:** `plt.show()` doesn't display anything
+
+**Solutions:**
+```python
+# 1. Check if backend is properly set (for interactive use)
+import matplotlib
+print(matplotlib.get_backend())
+
+# 2. Try different backends
+matplotlib.use('TkAgg')  # or 'Qt5Agg', 'MacOSX'
+import matplotlib.pyplot as plt
+
+# 3. In Jupyter notebooks, use magic command
+%matplotlib inline  # Static images
+# or
+%matplotlib widget  # Interactive plots
+
+# 4. Ensure plt.show() is called
+plt.plot([1, 2, 3])
+plt.show()
+```
+
+### Issue: "RuntimeError: main thread is not in main loop"
+
+**Problem:** Interactive mode issues with threading
+
+**Solution:**
+```python
+# Switch to non-interactive backend
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+# Or turn off interactive mode
+plt.ioff()
+```
+
+### Issue: Figures Not Updating Interactively
+
+**Problem:** Changes not reflected in interactive windows
+
+**Solution:**
+```python
+# Enable interactive mode
+plt.ion()
+
+# Draw after each change
+plt.plot(x, y)
+plt.draw()
+plt.pause(0.001)  # Brief pause to update display
+```
+
+## Layout and Spacing Issues
+
+### Issue: Overlapping Labels and Titles
+
+**Problem:** Labels, titles, or tick labels overlap or get cut off
+
+**Solutions:**
+```python
+# Solution 1: Constrained layout (RECOMMENDED)
+fig, ax = plt.subplots(constrained_layout=True)
+
+# Solution 2: Tight layout
+fig, ax = plt.subplots()
+plt.tight_layout()
+
+# Solution 3: Adjust margins manually
+plt.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+
+# Solution 4: Save with bbox_inches='tight'
+plt.savefig('figure.png', bbox_inches='tight')
+
+# Solution 5: Rotate long tick labels
+ax.set_xticklabels(labels, rotation=45, ha='right')
+```
+
+### Issue: Colorbar Affects Subplot Size
+
+**Problem:** Adding colorbar shrinks the plot
+
+**Solution:**
+```python
+# Solution 1: Use constrained layout
+fig, ax = plt.subplots(constrained_layout=True)
+im = ax.imshow(data)
+plt.colorbar(im, ax=ax)
+
+# Solution 2: Manually specify colorbar dimensions
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+divider = make_axes_locatable(ax)
+cax = divider.append_axes("right", size="5%", pad=0.05)
+plt.colorbar(im, cax=cax)
+
+# Solution 3: For multiple subplots, share colorbar
+fig, axes = plt.subplots(1, 3, figsize=(15, 4))
+for ax in axes:
+    im = ax.imshow(data)
+fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95)
+```
+
+### Issue: Subplots Too Close Together
+
+**Problem:** Multiple subplots overlapping
+
+**Solution:**
+```python
+# Solution 1: Use constrained_layout
+fig, axes = plt.subplots(2, 2, constrained_layout=True)
+
+# Solution 2: Adjust spacing with subplots_adjust
+fig, axes = plt.subplots(2, 2)
+plt.subplots_adjust(hspace=0.4, wspace=0.4)
+
+# Solution 3: Specify spacing in tight_layout
+plt.tight_layout(h_pad=2.0, w_pad=2.0)
+```
+
+## Memory and Performance Issues
+
+### Issue: Memory Leak with Multiple Figures
+
+**Problem:** Memory usage grows when creating many figures
+
+**Solution:**
+```python
+# Close figures explicitly
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.savefig('plot.png')
+plt.close(fig)  # or plt.close('all')
+
+# Clear current figure without closing
+plt.clf()
+
+# Clear current axes
+plt.cla()
+```
+
+### Issue: Large File Sizes
+
+**Problem:** Saved figures are too large
+
+**Solutions:**
+```python
+# Solution 1: Reduce DPI
+plt.savefig('figure.png', dpi=150)  # Instead of 300
+
+# Solution 2: Use rasterization for complex plots
+ax.plot(x, y, rasterized=True)
+
+# Solution 3: Use vector format for simple plots
+plt.savefig('figure.pdf')  # or .svg
+
+# Solution 4: Compress PNG
+plt.savefig('figure.png', dpi=300, optimize=True)
+```
+
+### Issue: Slow Plotting with Large Datasets
+
+**Problem:** Plotting takes too long with many points
+
+**Solutions:**
+```python
+# Solution 1: Downsample data
+from scipy.signal import decimate
+y_downsampled = decimate(y, 10)  # Keep every 10th point
+
+# Solution 2: Use rasterization
+ax.plot(x, y, rasterized=True)
+
+# Solution 3: Use line simplification
+ax.plot(x, y)
+for line in ax.get_lines():
+    line.set_rasterized(True)
+
+# Solution 4: For scatter plots, consider hexbin or 2d histogram
+ax.hexbin(x, y, gridsize=50, cmap='viridis')
+```
+
+## Font and Text Issues
+
+### Issue: Font Warnings
+
+**Problem:** "findfont: Font family [...] not found"
+
+**Solutions:**
+```python
+# Solution 1: Use available fonts
+from matplotlib.font_manager import findfont, FontProperties
+print(findfont(FontProperties(family='sans-serif')))
+
+# Solution 2: Rebuild font cache
+import matplotlib.font_manager
+matplotlib.font_manager._rebuild()
+
+# Solution 3: Suppress warnings
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# Solution 4: Specify fallback fonts
+plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'sans-serif']
+```
+
+### Issue: LaTeX Rendering Errors
+
+**Problem:** Math text not rendering correctly
+
+**Solutions:**
+```python
+# Solution 1: Use raw strings with r prefix
+ax.set_xlabel(r'$\alpha$')  # Not '\alpha'
+
+# Solution 2: Escape backslashes in regular strings
+ax.set_xlabel('$\\alpha$')
+
+# Solution 3: Disable LaTeX if not installed
+plt.rcParams['text.usetex'] = False
+
+# Solution 4: Use mathtext instead of full LaTeX
+# Mathtext is always available, no LaTeX installation needed
+ax.text(x, y, r'$\int_0^\infty e^{-x} dx$')
+```
+
+### Issue: Text Cut Off or Outside Figure
+
+**Problem:** Labels or annotations appear outside figure bounds
+
+**Solutions:**
+```python
+# Solution 1: Use bbox_inches='tight'
+plt.savefig('figure.png', bbox_inches='tight')
+
+# Solution 2: Adjust figure bounds
+plt.subplots_adjust(left=0.15, right=0.85, top=0.85, bottom=0.15)
+
+# Solution 3: Clip text to axes
+ax.text(x, y, 'text', clip_on=True)
+
+# Solution 4: Use constrained_layout
+fig, ax = plt.subplots(constrained_layout=True)
+```
+
+## Color and Colormap Issues
+
+### Issue: Colorbar Not Matching Plot
+
+**Problem:** Colorbar shows different range than data
+
+**Solution:**
+```python
+# Explicitly set vmin and vmax
+im = ax.imshow(data, vmin=0, vmax=1, cmap='viridis')
+plt.colorbar(im, ax=ax)
+
+# Or use the same norm for multiple plots
+import matplotlib.colors as mcolors
+norm = mcolors.Normalize(vmin=data.min(), vmax=data.max())
+im1 = ax1.imshow(data1, norm=norm, cmap='viridis')
+im2 = ax2.imshow(data2, norm=norm, cmap='viridis')
+```
+
+### Issue: Colors Look Wrong
+
+**Problem:** Unexpected colors in plots
+
+**Solutions:**
+```python
+# Solution 1: Check color specification format
+ax.plot(x, y, color='blue')  # Correct
+ax.plot(x, y, color=(0, 0, 1))  # Correct RGB
+ax.plot(x, y, color='#0000FF')  # Correct hex
+
+# Solution 2: Verify colormap exists
+print(plt.colormaps())  # List available colormaps
+
+# Solution 3: For scatter plots, ensure c shape matches
+ax.scatter(x, y, c=colors)  # colors should have same length as x, y
+
+# Solution 4: Check if alpha is set correctly
+ax.plot(x, y, alpha=1.0)  # 0=transparent, 1=opaque
+```
+
+### Issue: Reversed Colormap
+
+**Problem:** Colormap direction is backwards
+
+**Solution:**
+```python
+# Add _r suffix to reverse any colormap
+ax.imshow(data, cmap='viridis_r')
+```
+
+## Axis and Scale Issues
+
+### Issue: Axis Limits Not Working
+
+**Problem:** `set_xlim` or `set_ylim` not taking effect
+
+**Solutions:**
+```python
+# Solution 1: Set after plotting
+ax.plot(x, y)
+ax.set_xlim(0, 10)
+ax.set_ylim(-1, 1)
+
+# Solution 2: Disable autoscaling
+ax.autoscale(False)
+ax.set_xlim(0, 10)
+
+# Solution 3: Use axis method
+ax.axis([xmin, xmax, ymin, ymax])
+```
+
+### Issue: Log Scale with Zero or Negative Values
+
+**Problem:** ValueError when using log scale with data ≤ 0
+
+**Solutions:**
+```python
+# Solution 1: Filter out non-positive values
+mask = (data > 0)
+ax.plot(x[mask], data[mask])
+ax.set_yscale('log')
+
+# Solution 2: Use symlog for data with positive and negative values
+ax.set_yscale('symlog')
+
+# Solution 3: Add small offset
+ax.plot(x, data + 1e-10)
+ax.set_yscale('log')
+```
+
+### Issue: Dates Not Displaying Correctly
+
+**Problem:** Date axis shows numbers instead of dates
+
+**Solution:**
+```python
+import matplotlib.dates as mdates
+import pandas as pd
+
+# Convert to datetime if needed
+dates = pd.to_datetime(date_strings)
+
+ax.plot(dates, values)
+
+# Format date axis
+ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
+plt.xticks(rotation=45)
+```
+
+## Legend Issues
+
+### Issue: Legend Covers Data
+
+**Problem:** Legend obscures important parts of plot
+
+**Solutions:**
+```python
+# Solution 1: Use 'best' location
+ax.legend(loc='best')
+
+# Solution 2: Place outside plot area
+ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+
+# Solution 3: Make legend semi-transparent
+ax.legend(framealpha=0.7)
+
+# Solution 4: Put legend below plot
+ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3)
+```
+
+### Issue: Too Many Items in Legend
+
+**Problem:** Legend is cluttered with many entries
+
+**Solutions:**
+```python
+# Solution 1: Only label selected items
+for i, (x, y) in enumerate(data):
+    label = f'Data {i}' if i % 5 == 0 else None
+    ax.plot(x, y, label=label)
+
+# Solution 2: Use multiple columns
+ax.legend(ncol=3)
+
+# Solution 3: Create custom legend with fewer entries
+from matplotlib.lines import Line2D
+custom_lines = [Line2D([0], [0], color='r'),
+                Line2D([0], [0], color='b')]
+ax.legend(custom_lines, ['Category A', 'Category B'])
+
+# Solution 4: Use separate legend figure
+fig_leg = plt.figure(figsize=(3, 2))
+ax_leg = fig_leg.add_subplot(111)
+ax_leg.legend(*ax.get_legend_handles_labels(), loc='center')
+ax_leg.axis('off')
+```
+
+## 3D Plot Issues
+
+### Issue: 3D Plots Look Flat
+
+**Problem:** Difficult to perceive depth in 3D plots
+
+**Solutions:**
+```python
+# Solution 1: Adjust viewing angle
+ax.view_init(elev=30, azim=45)
+
+# Solution 2: Add gridlines
+ax.grid(True)
+
+# Solution 3: Use color for depth
+scatter = ax.scatter(x, y, z, c=z, cmap='viridis')
+
+# Solution 4: Rotate interactively (if using interactive backend)
+# User can click and drag to rotate
+```
+
+### Issue: 3D Axis Labels Cut Off
+
+**Problem:** 3D axis labels appear outside figure
+
+**Solution:**
+```python
+from mpl_toolkits.mplot3d import Axes3D
+
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.plot_surface(X, Y, Z)
+
+# Add padding
+fig.tight_layout(pad=3.0)
+
+# Or save with tight bounding box
+plt.savefig('3d_plot.png', bbox_inches='tight', pad_inches=0.5)
+```
+
+## Image and Colorbar Issues
+
+### Issue: Images Appear Flipped
+
+**Problem:** Image orientation is wrong
+
+**Solution:**
+```python
+# Set origin parameter
+ax.imshow(img, origin='lower')  # or 'upper' (default)
+
+# Or flip array
+ax.imshow(np.flipud(img))
+```
+
+### Issue: Images Look Pixelated
+
+**Problem:** Image appears blocky when zoomed
+
+**Solutions:**
+```python
+# Solution 1: Use interpolation
+ax.imshow(img, interpolation='bilinear')
+# Options: 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', etc.
+
+# Solution 2: Increase DPI when saving
+plt.savefig('figure.png', dpi=300)
+
+# Solution 3: Use vector format if appropriate
+plt.savefig('figure.pdf')
+```
+
+## Common Errors and Fixes
+
+### "TypeError: 'AxesSubplot' object is not subscriptable"
+
+**Problem:** Trying to index single axes
+```python
+# Wrong
+fig, ax = plt.subplots()
+ax[0].plot(x, y)  # Error!
+
+# Correct
+fig, ax = plt.subplots()
+ax.plot(x, y)
+```
+
+### "ValueError: x and y must have same first dimension"
+
+**Problem:** Data arrays have mismatched lengths
+```python
+# Check shapes
+print(f"x shape: {x.shape}, y shape: {y.shape}")
+
+# Ensure they match
+assert len(x) == len(y), "x and y must have same length"
+```
+
+### "AttributeError: 'numpy.ndarray' object has no attribute 'plot'"
+
+**Problem:** Calling plot on array instead of axes
+```python
+# Wrong
+data.plot(x, y)
+
+# Correct
+ax.plot(x, y)
+# or for pandas
+data.plot(ax=ax)
+```
+
+## Best Practices to Avoid Issues
+
+1. **Always use the OO interface** - Avoid pyplot state machine
+   ```python
+   fig, ax = plt.subplots()  # Good
+   ax.plot(x, y)
+   ```
+
+2. **Use constrained_layout** - Prevents overlap issues
+   ```python
+   fig, ax = plt.subplots(constrained_layout=True)
+   ```
+
+3. **Close figures explicitly** - Prevents memory leaks
+   ```python
+   plt.close(fig)
+   ```
+
+4. **Set figure size at creation** - Better than resizing later
+   ```python
+   fig, ax = plt.subplots(figsize=(10, 6))
+   ```
+
+5. **Use raw strings for math text** - Avoids escape issues
+   ```python
+   ax.set_xlabel(r'$\alpha$')
+   ```
+
+6. **Check data shapes before plotting** - Catch size mismatches early
+   ```python
+   assert len(x) == len(y)
+   ```
+
+7. **Use appropriate DPI** - 300 for print, 150 for web
+   ```python
+   plt.savefig('figure.png', dpi=300)
+   ```
+
+8. **Test with different backends** - If display issues occur
+   ```python
+   import matplotlib
+   matplotlib.use('TkAgg')
+   ```
--- a/skills/matplotlib/references/plot_types.md
+++ b/skills/matplotlib/references/plot_types.md
@@ -0,0 +1,476 @@
+# Matplotlib Plot Types Guide
+
+Comprehensive guide to different plot types in matplotlib with examples and use cases.
+
+## 1. Line Plots
+
+**Use cases:** Time series, continuous data, trends, function visualization
+
+### Basic Line Plot
+```python
+fig, ax = plt.subplots(figsize=(10, 6))
+ax.plot(x, y, linewidth=2, label='Data')
+ax.set_xlabel('X axis')
+ax.set_ylabel('Y axis')
+ax.legend()
+```
+
+### Multiple Lines
+```python
+ax.plot(x, y1, label='Dataset 1', linewidth=2)
+ax.plot(x, y2, label='Dataset 2', linewidth=2, linestyle='--')
+ax.plot(x, y3, label='Dataset 3', linewidth=2, linestyle=':')
+ax.legend()
+```
+
+### Line with Markers
+```python
+ax.plot(x, y, marker='o', markersize=8, linestyle='-',
+        linewidth=2, markerfacecolor='red', markeredgecolor='black')
+```
+
+### Step Plot
+```python
+ax.step(x, y, where='mid', linewidth=2, label='Step function')
+# where options: 'pre', 'post', 'mid'
+```
+
+### Error Bars
+```python
+ax.errorbar(x, y, yerr=error, fmt='o-', linewidth=2,
+            capsize=5, capthick=2, label='With uncertainty')
+```
+
+## 2. Scatter Plots
+
+**Use cases:** Correlations, relationships between variables, clusters, outliers
+
+### Basic Scatter
+```python
+ax.scatter(x, y, s=50, alpha=0.6)
+```
+
+### Sized and Colored Scatter
+```python
+scatter = ax.scatter(x, y, s=sizes*100, c=colors,
+                     cmap='viridis', alpha=0.6, edgecolors='black')
+plt.colorbar(scatter, ax=ax, label='Color variable')
+```
+
+### Categorical Scatter
+```python
+for category in categories:
+    mask = data['category'] == category
+    ax.scatter(data[mask]['x'], data[mask]['y'],
+               label=category, s=50, alpha=0.7)
+ax.legend()
+```
+
+## 3. Bar Charts
+
+**Use cases:** Categorical comparisons, discrete data, counts
+
+### Vertical Bar Chart
+```python
+ax.bar(categories, values, color='steelblue',
+       edgecolor='black', linewidth=1.5)
+ax.set_ylabel('Values')
+```
+
+### Horizontal Bar Chart
+```python
+ax.barh(categories, values, color='coral',
+        edgecolor='black', linewidth=1.5)
+ax.set_xlabel('Values')
+```
+
+### Grouped Bar Chart
+```python
+x = np.arange(len(categories))
+width = 0.35
+
+ax.bar(x - width/2, values1, width, label='Group 1')
+ax.bar(x + width/2, values2, width, label='Group 2')
+ax.set_xticks(x)
+ax.set_xticklabels(categories)
+ax.legend()
+```
+
+### Stacked Bar Chart
+```python
+ax.bar(categories, values1, label='Part 1')
+ax.bar(categories, values2, bottom=values1, label='Part 2')
+ax.bar(categories, values3, bottom=values1+values2, label='Part 3')
+ax.legend()
+```
+
+### Bar Chart with Error Bars
+```python
+ax.bar(categories, values, yerr=errors, capsize=5,
+       color='steelblue', edgecolor='black')
+```
+
+### Bar Chart with Patterns
+```python
+bars1 = ax.bar(x - width/2, values1, width, label='Group 1',
+               color='white', edgecolor='black', hatch='//')
+bars2 = ax.bar(x + width/2, values2, width, label='Group 2',
+               color='white', edgecolor='black', hatch='\\\\')
+```
+
+## 4. Histograms
+
+**Use cases:** Distributions, frequency analysis
+
+### Basic Histogram
+```python
+ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
+ax.set_xlabel('Value')
+ax.set_ylabel('Frequency')
+```
+
+### Multiple Overlapping Histograms
+```python
+ax.hist(data1, bins=30, alpha=0.5, label='Dataset 1')
+ax.hist(data2, bins=30, alpha=0.5, label='Dataset 2')
+ax.legend()
+```
+
+### Normalized Histogram (Density)
+```python
+ax.hist(data, bins=30, density=True, alpha=0.7,
+        edgecolor='black', label='Empirical')
+
+# Overlay theoretical distribution
+from scipy.stats import norm
+x = np.linspace(data.min(), data.max(), 100)
+ax.plot(x, norm.pdf(x, data.mean(), data.std()),
+        'r-', linewidth=2, label='Normal fit')
+ax.legend()
+```
+
+### 2D Histogram (Hexbin)
+```python
+hexbin = ax.hexbin(x, y, gridsize=30, cmap='Blues')
+plt.colorbar(hexbin, ax=ax, label='Counts')
+```
+
+### 2D Histogram (hist2d)
+```python
+h = ax.hist2d(x, y, bins=30, cmap='Blues')
+plt.colorbar(h[3], ax=ax, label='Counts')
+```
+
+## 5. Box and Violin Plots
+
+**Use cases:** Statistical distributions, outlier detection, comparing distributions
+
+### Box Plot
+```python
+ax.boxplot([data1, data2, data3],
+           labels=['Group A', 'Group B', 'Group C'],
+           showmeans=True, meanline=True)
+ax.set_ylabel('Values')
+```
+
+### Horizontal Box Plot
+```python
+ax.boxplot([data1, data2, data3], vert=False,
+           labels=['Group A', 'Group B', 'Group C'])
+ax.set_xlabel('Values')
+```
+
+### Violin Plot
+```python
+parts = ax.violinplot([data1, data2, data3],
+                      positions=[1, 2, 3],
+                      showmeans=True, showmedians=True)
+ax.set_xticks([1, 2, 3])
+ax.set_xticklabels(['Group A', 'Group B', 'Group C'])
+```
+
+## 6. Heatmaps
+
+**Use cases:** Matrix data, correlations, intensity maps
+
+### Basic Heatmap
+```python
+im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
+plt.colorbar(im, ax=ax, label='Values')
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+```
+
+### Heatmap with Annotations
+```python
+im = ax.imshow(matrix, cmap='coolwarm')
+plt.colorbar(im, ax=ax)
+
+# Add text annotations
+for i in range(matrix.shape[0]):
+    for j in range(matrix.shape[1]):
+        text = ax.text(j, i, f'{matrix[i, j]:.2f}',
+                       ha='center', va='center', color='black')
+```
+
+### Correlation Matrix
+```python
+corr = data.corr()
+im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
+plt.colorbar(im, ax=ax, label='Correlation')
+
+# Set tick labels
+ax.set_xticks(range(len(corr)))
+ax.set_yticks(range(len(corr)))
+ax.set_xticklabels(corr.columns, rotation=45, ha='right')
+ax.set_yticklabels(corr.columns)
+```
+
+## 7. Contour Plots
+
+**Use cases:** 3D data on 2D plane, topography, function visualization
+
+### Contour Lines
+```python
+contour = ax.contour(X, Y, Z, levels=10, cmap='viridis')
+ax.clabel(contour, inline=True, fontsize=8)
+plt.colorbar(contour, ax=ax)
+```
+
+### Filled Contours
+```python
+contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis')
+plt.colorbar(contourf, ax=ax)
+```
+
+### Combined Contours
+```python
+contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis', alpha=0.8)
+contour = ax.contour(X, Y, Z, levels=10, colors='black',
+                     linewidths=0.5, alpha=0.4)
+ax.clabel(contour, inline=True, fontsize=8)
+plt.colorbar(contourf, ax=ax)
+```
+
+## 8. Pie Charts
+
+**Use cases:** Proportions, percentages (use sparingly)
+
+### Basic Pie Chart
+```python
+ax.pie(sizes, labels=labels, autopct='%1.1f%%',
+       startangle=90, colors=colors)
+ax.axis('equal')  # Equal aspect ratio ensures circular pie
+```
+
+### Exploded Pie Chart
+```python
+explode = (0.1, 0, 0, 0)  # Explode first slice
+ax.pie(sizes, explode=explode, labels=labels,
+       autopct='%1.1f%%', shadow=True, startangle=90)
+ax.axis('equal')
+```
+
+### Donut Chart
+```python
+ax.pie(sizes, labels=labels, autopct='%1.1f%%',
+       wedgeprops=dict(width=0.5), startangle=90)
+ax.axis('equal')
+```
+
+## 9. Polar Plots
+
+**Use cases:** Cyclic data, directional data, radar charts
+
+### Basic Polar Plot
+```python
+theta = np.linspace(0, 2*np.pi, 100)
+r = np.abs(np.sin(2*theta))
+
+ax = plt.subplot(111, projection='polar')
+ax.plot(theta, r, linewidth=2)
+```
+
+### Radar Chart
+```python
+categories = ['A', 'B', 'C', 'D', 'E']
+values = [4, 3, 5, 2, 4]
+
+# Add first value to the end to close the polygon
+angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False)
+values_closed = np.concatenate((values, [values[0]]))
+angles_closed = np.concatenate((angles, [angles[0]]))
+
+ax = plt.subplot(111, projection='polar')
+ax.plot(angles_closed, values_closed, 'o-', linewidth=2)
+ax.fill(angles_closed, values_closed, alpha=0.25)
+ax.set_xticks(angles)
+ax.set_xticklabels(categories)
+```
+
+## 10. Stream and Quiver Plots
+
+**Use cases:** Vector fields, flow visualization
+
+### Quiver Plot (Vector Field)
+```python
+ax.quiver(X, Y, U, V, alpha=0.8)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_aspect('equal')
+```
+
+### Stream Plot
+```python
+ax.streamplot(X, Y, U, V, density=1.5, color='k', linewidth=1)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_aspect('equal')
+```
+
+## 11. Fill Between
+
+**Use cases:** Uncertainty bounds, confidence intervals, areas under curves
+
+### Fill Between Two Curves
+```python
+ax.plot(x, y, 'k-', linewidth=2, label='Mean')
+ax.fill_between(x, y - std, y + std, alpha=0.3,
+                label='±1 std dev')
+ax.legend()
+```
+
+### Fill Between with Condition
+```python
+ax.plot(x, y1, label='Line 1')
+ax.plot(x, y2, label='Line 2')
+ax.fill_between(x, y1, y2, where=(y2 >= y1),
+                alpha=0.3, label='y2 > y1', interpolate=True)
+ax.legend()
+```
+
+## 12. 3D Plots
+
+**Use cases:** Three-dimensional data visualization
+
+### 3D Scatter
+```python
+from mpl_toolkits.mplot3d import Axes3D
+
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+scatter = ax.scatter(x, y, z, c=colors, cmap='viridis',
+                     marker='o', s=50)
+plt.colorbar(scatter, ax=ax)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+```
+
+### 3D Surface Plot
+```python
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+surf = ax.plot_surface(X, Y, Z, cmap='viridis',
+                       edgecolor='none', alpha=0.9)
+plt.colorbar(surf, ax=ax)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+```
+
+### 3D Wireframe
+```python
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.plot_wireframe(X, Y, Z, color='black', linewidth=0.5)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+```
+
+### 3D Contour
+```python
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.contour(X, Y, Z, levels=15, cmap='viridis')
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+```
+
+## 13. Specialized Plots
+
+### Stem Plot
+```python
+ax.stem(x, y, linefmt='C0-', markerfmt='C0o', basefmt='k-')
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+```
+
+### Filled Polygon
+```python
+vertices = [(0, 0), (1, 0), (1, 1), (0, 1)]
+from matplotlib.patches import Polygon
+polygon = Polygon(vertices, closed=True, edgecolor='black',
+                  facecolor='lightblue', alpha=0.5)
+ax.add_patch(polygon)
+ax.set_xlim(-0.5, 1.5)
+ax.set_ylim(-0.5, 1.5)
+```
+
+### Staircase Plot
+```python
+ax.stairs(values, edges, fill=True, alpha=0.5)
+```
+
+### Broken Barh (Gantt-style)
+```python
+ax.broken_barh([(10, 50), (100, 20), (130, 10)], (10, 9),
+               facecolors='tab:blue')
+ax.broken_barh([(10, 20), (50, 50), (120, 30)], (20, 9),
+               facecolors='tab:orange')
+ax.set_ylim(5, 35)
+ax.set_xlim(0, 200)
+ax.set_xlabel('Time')
+ax.set_yticks([15, 25])
+ax.set_yticklabels(['Task 1', 'Task 2'])
+```
+
+## 14. Time Series Plots
+
+### Basic Time Series
+```python
+import pandas as pd
+import matplotlib.dates as mdates
+
+ax.plot(dates, values, linewidth=2)
+ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
+plt.xticks(rotation=45)
+ax.set_xlabel('Date')
+ax.set_ylabel('Value')
+```
+
+### Time Series with Shaded Regions
+```python
+ax.plot(dates, values, linewidth=2)
+# Shade weekends or specific periods
+ax.axvspan(start_date, end_date, alpha=0.2, color='gray')
+```
+
+## Plot Selection Guide
+
+| Data Type | Recommended Plot | Alternative Options |
+|-----------|-----------------|---------------------|
+| Single continuous variable | Histogram, KDE | Box plot, Violin plot |
+| Two continuous variables | Scatter plot | Hexbin, 2D histogram |
+| Time series | Line plot | Area plot, Step plot |
+| Categorical vs continuous | Bar chart, Box plot | Violin plot, Strip plot |
+| Two categorical variables | Heatmap | Grouped bar chart |
+| Three continuous variables | 3D scatter, Contour | Color-coded scatter |
+| Proportions | Bar chart | Pie chart (use sparingly) |
+| Distributions comparison | Box plot, Violin plot | Overlaid histograms |
+| Correlation matrix | Heatmap | Clustered heatmap |
+| Vector field | Quiver plot, Stream plot | - |
+| Function visualization | Line plot, Contour | 3D surface |
--- a/skills/matplotlib/references/styling_guide.md
+++ b/skills/matplotlib/references/styling_guide.md
@@ -0,0 +1,589 @@
+# Matplotlib Styling Guide
+
+Comprehensive guide for styling and customizing matplotlib visualizations.
+
+## Colormaps
+
+### Colormap Categories
+
+**1. Perceptually Uniform Sequential**
+Best for ordered data that progresses from low to high values.
+- `viridis` (default, colorblind-friendly)
+- `plasma`
+- `inferno`
+- `magma`
+- `cividis` (optimized for colorblind viewers)
+
+**Usage:**
+```python
+im = ax.imshow(data, cmap='viridis')
+scatter = ax.scatter(x, y, c=values, cmap='plasma')
+```
+
+**2. Sequential**
+Traditional colormaps for ordered data.
+- `Blues`, `Greens`, `Reds`, `Oranges`, `Purples`
+- `YlOrBr`, `YlOrRd`, `OrRd`, `PuRd`
+- `BuPu`, `GnBu`, `PuBu`, `YlGnBu`
+
+**3. Diverging**
+Best for data with a meaningful center point (e.g., zero, mean).
+- `coolwarm` (blue to red)
+- `RdBu` (red-blue)
+- `RdYlBu` (red-yellow-blue)
+- `RdYlGn` (red-yellow-green)
+- `PiYG`, `PRGn`, `BrBG`, `PuOr`, `RdGy`
+
+**Usage:**
+```python
+# Center colormap at zero
+im = ax.imshow(data, cmap='coolwarm', vmin=-1, vmax=1)
+```
+
+**4. Qualitative**
+Best for categorical/nominal data without inherent ordering.
+- `tab10` (10 distinct colors)
+- `tab20` (20 distinct colors)
+- `Set1`, `Set2`, `Set3`
+- `Pastel1`, `Pastel2`
+- `Dark2`, `Accent`, `Paired`
+
+**Usage:**
+```python
+colors = plt.cm.tab10(np.linspace(0, 1, n_categories))
+for i, category in enumerate(categories):
+    ax.plot(x, y[i], color=colors[i], label=category)
+```
+
+**5. Cyclic**
+Best for cyclic data (e.g., phase, angle).
+- `twilight`
+- `twilight_shifted`
+- `hsv`
+
+### Colormap Best Practices
+
+1. **Avoid `jet` colormap** - Not perceptually uniform, misleading
+2. **Use perceptually uniform colormaps** - `viridis`, `plasma`, `cividis`
+3. **Consider colorblind users** - Use `viridis`, `cividis`, or test with colorblind simulators
+4. **Match colormap to data type**:
+   - Sequential: increasing/decreasing data
+   - Diverging: data with meaningful center
+   - Qualitative: categories
+5. **Reverse colormaps** - Add `_r` suffix: `viridis_r`, `coolwarm_r`
+
+### Creating Custom Colormaps
+
+```python
+from matplotlib.colors import LinearSegmentedColormap
+
+# From color list
+colors = ['blue', 'white', 'red']
+n_bins = 100
+cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
+
+# From RGB values
+colors = [(0, 0, 1), (1, 1, 1), (1, 0, 0)]  # RGB tuples
+cmap = LinearSegmentedColormap.from_list('custom', colors)
+
+# Use the custom colormap
+ax.imshow(data, cmap=cmap)
+```
+
+### Discrete Colormaps
+
+```python
+import matplotlib.colors as mcolors
+
+# Create discrete colormap from continuous
+cmap = plt.cm.viridis
+bounds = np.linspace(0, 10, 11)
+norm = mcolors.BoundaryNorm(bounds, cmap.N)
+im = ax.imshow(data, cmap=cmap, norm=norm)
+```
+
+## Style Sheets
+
+### Using Built-in Styles
+
+```python
+# List available styles
+print(plt.style.available)
+
+# Apply a style
+plt.style.use('seaborn-v0_8-darkgrid')
+
+# Apply multiple styles (later styles override earlier ones)
+plt.style.use(['seaborn-v0_8-whitegrid', 'seaborn-v0_8-poster'])
+
+# Temporarily use a style
+with plt.style.context('ggplot'):
+    fig, ax = plt.subplots()
+    ax.plot(x, y)
+```
+
+### Popular Built-in Styles
+
+- `default` - Matplotlib's default style
+- `classic` - Classic matplotlib look (pre-2.0)
+- `seaborn-v0_8-*` - Seaborn-inspired styles
+  - `seaborn-v0_8-darkgrid`, `seaborn-v0_8-whitegrid`
+  - `seaborn-v0_8-dark`, `seaborn-v0_8-white`
+  - `seaborn-v0_8-ticks`, `seaborn-v0_8-poster`, `seaborn-v0_8-talk`
+- `ggplot` - ggplot2-inspired style
+- `bmh` - Bayesian Methods for Hackers style
+- `fivethirtyeight` - FiveThirtyEight style
+- `grayscale` - Grayscale style
+
+### Creating Custom Style Sheets
+
+Create a file named `custom_style.mplstyle`:
+
+```
+# custom_style.mplstyle
+
+# Figure
+figure.figsize: 10, 6
+figure.dpi: 100
+figure.facecolor: white
+
+# Font
+font.family: sans-serif
+font.sans-serif: Arial, Helvetica
+font.size: 12
+
+# Axes
+axes.labelsize: 14
+axes.titlesize: 16
+axes.facecolor: white
+axes.edgecolor: black
+axes.linewidth: 1.5
+axes.grid: True
+axes.axisbelow: True
+
+# Grid
+grid.color: gray
+grid.linestyle: --
+grid.linewidth: 0.5
+grid.alpha: 0.3
+
+# Lines
+lines.linewidth: 2
+lines.markersize: 8
+
+# Ticks
+xtick.labelsize: 10
+ytick.labelsize: 10
+xtick.direction: in
+ytick.direction: in
+xtick.major.size: 6
+ytick.major.size: 6
+xtick.minor.size: 3
+ytick.minor.size: 3
+
+# Legend
+legend.fontsize: 12
+legend.frameon: True
+legend.framealpha: 0.8
+legend.fancybox: True
+
+# Savefig
+savefig.dpi: 300
+savefig.bbox: tight
+savefig.facecolor: white
+```
+
+Load and use:
+```python
+plt.style.use('path/to/custom_style.mplstyle')
+```
+
+## rcParams Configuration
+
+### Global Configuration
+
+```python
+import matplotlib.pyplot as plt
+
+# Configure globally
+plt.rcParams['figure.figsize'] = (10, 6)
+plt.rcParams['font.size'] = 12
+plt.rcParams['axes.labelsize'] = 14
+
+# Or update multiple at once
+plt.rcParams.update({
+    'figure.figsize': (10, 6),
+    'font.size': 12,
+    'axes.labelsize': 14,
+    'axes.titlesize': 16,
+    'lines.linewidth': 2
+})
+```
+
+### Temporary Configuration
+
+```python
+# Context manager for temporary changes
+with plt.rc_context({'font.size': 14, 'lines.linewidth': 2.5}):
+    fig, ax = plt.subplots()
+    ax.plot(x, y)
+```
+
+### Common rcParams
+
+**Figure settings:**
+```python
+plt.rcParams['figure.figsize'] = (10, 6)
+plt.rcParams['figure.dpi'] = 100
+plt.rcParams['figure.facecolor'] = 'white'
+plt.rcParams['figure.edgecolor'] = 'white'
+plt.rcParams['figure.autolayout'] = False
+plt.rcParams['figure.constrained_layout.use'] = True
+```
+
+**Font settings:**
+```python
+plt.rcParams['font.family'] = 'sans-serif'
+plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'DejaVu Sans']
+plt.rcParams['font.size'] = 12
+plt.rcParams['font.weight'] = 'normal'
+```
+
+**Axes settings:**
+```python
+plt.rcParams['axes.facecolor'] = 'white'
+plt.rcParams['axes.edgecolor'] = 'black'
+plt.rcParams['axes.linewidth'] = 1.5
+plt.rcParams['axes.grid'] = True
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['axes.titlesize'] = 16
+plt.rcParams['axes.labelweight'] = 'normal'
+plt.rcParams['axes.spines.top'] = True
+plt.rcParams['axes.spines.right'] = True
+```
+
+**Line settings:**
+```python
+plt.rcParams['lines.linewidth'] = 2
+plt.rcParams['lines.linestyle'] = '-'
+plt.rcParams['lines.marker'] = 'None'
+plt.rcParams['lines.markersize'] = 6
+```
+
+**Save settings:**
+```python
+plt.rcParams['savefig.dpi'] = 300
+plt.rcParams['savefig.format'] = 'png'
+plt.rcParams['savefig.bbox'] = 'tight'
+plt.rcParams['savefig.pad_inches'] = 0.1
+plt.rcParams['savefig.transparent'] = False
+```
+
+## Color Palettes
+
+### Named Color Sets
+
+```python
+# Tableau colors
+tableau_colors = plt.cm.tab10.colors
+
+# CSS4 colors (subset)
+css_colors = ['steelblue', 'coral', 'teal', 'goldenrod', 'crimson']
+
+# Manual definition
+custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']
+```
+
+### Color Cycles
+
+```python
+# Set default color cycle
+from cycler import cycler
+colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+plt.rcParams['axes.prop_cycle'] = cycler(color=colors)
+
+# Or combine color and line style
+plt.rcParams['axes.prop_cycle'] = cycler(color=colors) + cycler(linestyle=['-', '--', ':', '-.'])
+```
+
+### Palette Generation
+
+```python
+# Evenly spaced colors from colormap
+n_colors = 5
+colors = plt.cm.viridis(np.linspace(0, 1, n_colors))
+
+# Use in plot
+for i, (x, y) in enumerate(data):
+    ax.plot(x, y, color=colors[i])
+```
+
+## Typography
+
+### Font Configuration
+
+```python
+# Set font family
+plt.rcParams['font.family'] = 'serif'
+plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif']
+
+# Or sans-serif
+plt.rcParams['font.family'] = 'sans-serif'
+plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
+
+# Or monospace
+plt.rcParams['font.family'] = 'monospace'
+plt.rcParams['font.monospace'] = ['Courier New', 'DejaVu Sans Mono']
+```
+
+### Font Properties in Text
+
+```python
+from matplotlib import font_manager
+
+# Specify font properties
+ax.text(x, y, 'Text',
+        fontsize=14,
+        fontweight='bold',  # 'normal', 'bold', 'heavy', 'light'
+        fontstyle='italic',  # 'normal', 'italic', 'oblique'
+        fontfamily='serif')
+
+# Use specific font file
+prop = font_manager.FontProperties(fname='path/to/font.ttf')
+ax.text(x, y, 'Text', fontproperties=prop)
+```
+
+### Mathematical Text
+
+```python
+# LaTeX-style math
+ax.set_title(r'$\alpha > \beta$')
+ax.set_xlabel(r'$\mu \pm \sigma$')
+ax.text(x, y, r'$\int_0^\infty e^{-x} dx = 1$')
+
+# Subscripts and superscripts
+ax.set_ylabel(r'$y = x^2 + 2x + 1$')
+ax.text(x, y, r'$x_1, x_2, \ldots, x_n$')
+
+# Greek letters
+ax.text(x, y, r'$\alpha, \beta, \gamma, \delta, \epsilon$')
+```
+
+### Using Full LaTeX
+
+```python
+# Enable full LaTeX rendering (requires LaTeX installation)
+plt.rcParams['text.usetex'] = True
+plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath}'
+
+ax.set_title(r'\textbf{Bold Title}')
+ax.set_xlabel(r'Time $t$ (s)')
+```
+
+## Spines and Grids
+
+### Spine Customization
+
+```python
+# Hide specific spines
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+
+# Move spine position
+ax.spines['left'].set_position(('outward', 10))
+ax.spines['bottom'].set_position(('data', 0))
+
+# Change spine color and width
+ax.spines['left'].set_color('red')
+ax.spines['bottom'].set_linewidth(2)
+```
+
+### Grid Customization
+
+```python
+# Basic grid
+ax.grid(True)
+
+# Customized grid
+ax.grid(True, which='major', linestyle='--', linewidth=0.8, alpha=0.3)
+ax.grid(True, which='minor', linestyle=':', linewidth=0.5, alpha=0.2)
+
+# Grid for specific axis
+ax.grid(True, axis='x')  # Only vertical lines
+ax.grid(True, axis='y')  # Only horizontal lines
+
+# Grid behind or in front of data
+ax.set_axisbelow(True)  # Grid behind data
+```
+
+## Legend Customization
+
+### Legend Positioning
+
+```python
+# Location strings
+ax.legend(loc='best')  # Automatic best position
+ax.legend(loc='upper right')
+ax.legend(loc='upper left')
+ax.legend(loc='lower right')
+ax.legend(loc='lower left')
+ax.legend(loc='center')
+ax.legend(loc='upper center')
+ax.legend(loc='lower center')
+ax.legend(loc='center left')
+ax.legend(loc='center right')
+
+# Precise positioning (bbox_to_anchor)
+ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')  # Outside plot area
+ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3)  # Below plot
+```
+
+### Legend Styling
+
+```python
+ax.legend(
+    fontsize=12,
+    frameon=True,           # Show frame
+    framealpha=0.9,         # Frame transparency
+    fancybox=True,          # Rounded corners
+    shadow=True,            # Shadow effect
+    ncol=2,                 # Number of columns
+    title='Legend Title',   # Legend title
+    title_fontsize=14,      # Title font size
+    edgecolor='black',      # Frame edge color
+    facecolor='white'       # Frame background color
+)
+```
+
+### Custom Legend Entries
+
+```python
+from matplotlib.lines import Line2D
+
+# Create custom legend handles
+custom_lines = [Line2D([0], [0], color='red', lw=2),
+                Line2D([0], [0], color='blue', lw=2, linestyle='--'),
+                Line2D([0], [0], marker='o', color='w', markerfacecolor='green', markersize=10)]
+
+ax.legend(custom_lines, ['Label 1', 'Label 2', 'Label 3'])
+```
+
+## Layout and Spacing
+
+### Constrained Layout
+
+```python
+# Preferred method (automatic adjustment)
+fig, axes = plt.subplots(2, 2, constrained_layout=True)
+```
+
+### Tight Layout
+
+```python
+# Alternative method
+fig, axes = plt.subplots(2, 2)
+plt.tight_layout(pad=1.5, h_pad=2.0, w_pad=2.0)
+```
+
+### Manual Adjustment
+
+```python
+# Fine-grained control
+plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
+                    hspace=0.3, wspace=0.4)
+```
+
+## Professional Publication Style
+
+Example configuration for publication-quality figures:
+
+```python
+# Publication style configuration
+plt.rcParams.update({
+    # Figure
+    'figure.figsize': (8, 6),
+    'figure.dpi': 100,
+    'savefig.dpi': 300,
+    'savefig.bbox': 'tight',
+    'savefig.pad_inches': 0.1,
+
+    # Font
+    'font.family': 'sans-serif',
+    'font.sans-serif': ['Arial', 'Helvetica'],
+    'font.size': 11,
+
+    # Axes
+    'axes.labelsize': 12,
+    'axes.titlesize': 14,
+    'axes.linewidth': 1.5,
+    'axes.grid': False,
+    'axes.spines.top': False,
+    'axes.spines.right': False,
+
+    # Lines
+    'lines.linewidth': 2,
+    'lines.markersize': 8,
+
+    # Ticks
+    'xtick.labelsize': 10,
+    'ytick.labelsize': 10,
+    'xtick.major.size': 6,
+    'ytick.major.size': 6,
+    'xtick.major.width': 1.5,
+    'ytick.major.width': 1.5,
+    'xtick.direction': 'in',
+    'ytick.direction': 'in',
+
+    # Legend
+    'legend.fontsize': 10,
+    'legend.frameon': True,
+    'legend.framealpha': 1.0,
+    'legend.edgecolor': 'black'
+})
+```
+
+## Dark Theme
+
+```python
+# Dark background style
+plt.style.use('dark_background')
+
+# Or manual configuration
+plt.rcParams.update({
+    'figure.facecolor': '#1e1e1e',
+    'axes.facecolor': '#1e1e1e',
+    'axes.edgecolor': 'white',
+    'axes.labelcolor': 'white',
+    'text.color': 'white',
+    'xtick.color': 'white',
+    'ytick.color': 'white',
+    'grid.color': 'gray',
+    'legend.facecolor': '#1e1e1e',
+    'legend.edgecolor': 'white'
+})
+```
+
+## Color Accessibility
+
+### Colorblind-Friendly Palettes
+
+```python
+# Use colorblind-friendly colormaps
+colorblind_friendly = ['viridis', 'plasma', 'cividis']
+
+# Colorblind-friendly discrete colors
+cb_colors = ['#0173B2', '#DE8F05', '#029E73', '#CC78BC',
+             '#CA9161', '#949494', '#ECE133', '#56B4E9']
+
+# Test with simulation tools or use these validated palettes
+```
+
+### High Contrast
+
+```python
+# Ensure sufficient contrast
+plt.rcParams['axes.edgecolor'] = 'black'
+plt.rcParams['axes.linewidth'] = 2
+plt.rcParams['xtick.major.width'] = 2
+plt.rcParams['ytick.major.width'] = 2
+```
--- a/skills/matplotlib/scripts/plot_template.py
+++ b/skills/matplotlib/scripts/plot_template.py
@@ -0,0 +1,401 @@
+#!/usr/bin/env python3
+"""
+Matplotlib Plot Template
+
+Comprehensive template demonstrating various plot types and best practices.
+Use this as a starting point for creating publication-quality visualizations.
+
+Usage:
+    python plot_template.py [--plot-type TYPE] [--style STYLE] [--output FILE]
+
+Plot types:
+    line, scatter, bar, histogram, heatmap, contour, box, violin, 3d, all
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+import argparse
+
+
+def set_publication_style():
+    """Configure matplotlib for publication-quality figures."""
+    plt.rcParams.update({
+        'figure.figsize': (10, 6),
+        'figure.dpi': 100,
+        'savefig.dpi': 300,
+        'savefig.bbox': 'tight',
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'axes.titlesize': 14,
+        'xtick.labelsize': 10,
+        'ytick.labelsize': 10,
+        'legend.fontsize': 10,
+        'lines.linewidth': 2,
+        'axes.linewidth': 1.5,
+    })
+
+
+def generate_sample_data():
+    """Generate sample data for demonstrations."""
+    np.random.seed(42)
+    x = np.linspace(0, 10, 100)
+    y1 = np.sin(x)
+    y2 = np.cos(x)
+    scatter_x = np.random.randn(200)
+    scatter_y = np.random.randn(200)
+    categories = ['A', 'B', 'C', 'D', 'E']
+    bar_values = np.random.randint(10, 100, len(categories))
+    hist_data = np.random.normal(0, 1, 1000)
+    matrix = np.random.rand(10, 10)
+
+    X, Y = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))
+    Z = np.sin(np.sqrt(X**2 + Y**2))
+
+    return {
+        'x': x, 'y1': y1, 'y2': y2,
+        'scatter_x': scatter_x, 'scatter_y': scatter_y,
+        'categories': categories, 'bar_values': bar_values,
+        'hist_data': hist_data, 'matrix': matrix,
+        'X': X, 'Y': Y, 'Z': Z
+    }
+
+
+def create_line_plot(data, ax=None):
+    """Create line plot with best practices."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    ax.plot(data['x'], data['y1'], label='sin(x)', linewidth=2, marker='o',
+            markevery=10, markersize=6)
+    ax.plot(data['x'], data['y2'], label='cos(x)', linewidth=2, linestyle='--')
+
+    ax.set_xlabel('x')
+    ax.set_ylabel('y')
+    ax.set_title('Line Plot Example')
+    ax.legend(loc='best', framealpha=0.9)
+    ax.grid(True, alpha=0.3, linestyle='--')
+
+    # Remove top and right spines for cleaner look
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_scatter_plot(data, ax=None):
+    """Create scatter plot with color and size variations."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    # Color based on distance from origin
+    colors = np.sqrt(data['scatter_x']**2 + data['scatter_y']**2)
+    sizes = 50 * (1 + np.abs(data['scatter_x']))
+
+    scatter = ax.scatter(data['scatter_x'], data['scatter_y'],
+                        c=colors, s=sizes, alpha=0.6,
+                        cmap='viridis', edgecolors='black', linewidth=0.5)
+
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_title('Scatter Plot Example')
+    ax.grid(True, alpha=0.3, linestyle='--')
+
+    # Add colorbar
+    cbar = plt.colorbar(scatter, ax=ax)
+    cbar.set_label('Distance from origin')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_bar_chart(data, ax=None):
+    """Create bar chart with error bars and styling."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    x_pos = np.arange(len(data['categories']))
+    errors = np.random.randint(5, 15, len(data['categories']))
+
+    bars = ax.bar(x_pos, data['bar_values'], yerr=errors,
+                  color='steelblue', edgecolor='black', linewidth=1.5,
+                  capsize=5, alpha=0.8)
+
+    # Color bars by value
+    colors = plt.cm.viridis(data['bar_values'] / data['bar_values'].max())
+    for bar, color in zip(bars, colors):
+        bar.set_facecolor(color)
+
+    ax.set_xlabel('Category')
+    ax.set_ylabel('Values')
+    ax.set_title('Bar Chart Example')
+    ax.set_xticks(x_pos)
+    ax.set_xticklabels(data['categories'])
+    ax.grid(True, axis='y', alpha=0.3, linestyle='--')
+
+    # Remove top and right spines
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_histogram(data, ax=None):
+    """Create histogram with density overlay."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    n, bins, patches = ax.hist(data['hist_data'], bins=30, density=True,
+                               alpha=0.7, edgecolor='black', color='steelblue')
+
+    # Overlay theoretical normal distribution
+    from scipy.stats import norm
+    mu, std = norm.fit(data['hist_data'])
+    x_theory = np.linspace(data['hist_data'].min(), data['hist_data'].max(), 100)
+    ax.plot(x_theory, norm.pdf(x_theory, mu, std), 'r-', linewidth=2,
+            label=f'Normal fit (μ={mu:.2f}, σ={std:.2f})')
+
+    ax.set_xlabel('Value')
+    ax.set_ylabel('Density')
+    ax.set_title('Histogram with Normal Fit')
+    ax.legend()
+    ax.grid(True, axis='y', alpha=0.3, linestyle='--')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_heatmap(data, ax=None):
+    """Create heatmap with colorbar and annotations."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 8), constrained_layout=True)
+
+    im = ax.imshow(data['matrix'], cmap='coolwarm', aspect='auto',
+                   vmin=0, vmax=1)
+
+    # Add colorbar
+    cbar = plt.colorbar(im, ax=ax)
+    cbar.set_label('Value')
+
+    # Optional: Add text annotations
+    # for i in range(data['matrix'].shape[0]):
+    #     for j in range(data['matrix'].shape[1]):
+    #         text = ax.text(j, i, f'{data["matrix"][i, j]:.2f}',
+    #                       ha='center', va='center', color='black', fontsize=8)
+
+    ax.set_xlabel('X Index')
+    ax.set_ylabel('Y Index')
+    ax.set_title('Heatmap Example')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_contour_plot(data, ax=None):
+    """Create contour plot with filled contours and labels."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 8), constrained_layout=True)
+
+    # Filled contours
+    contourf = ax.contourf(data['X'], data['Y'], data['Z'],
+                           levels=20, cmap='viridis', alpha=0.8)
+
+    # Contour lines
+    contour = ax.contour(data['X'], data['Y'], data['Z'],
+                        levels=10, colors='black', linewidths=0.5, alpha=0.4)
+
+    # Add labels to contour lines
+    ax.clabel(contour, inline=True, fontsize=8)
+
+    # Add colorbar
+    cbar = plt.colorbar(contourf, ax=ax)
+    cbar.set_label('Z value')
+
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_title('Contour Plot Example')
+    ax.set_aspect('equal')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_box_plot(data, ax=None):
+    """Create box plot comparing distributions."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    # Generate multiple distributions
+    box_data = [np.random.normal(0, std, 100) for std in range(1, 5)]
+
+    bp = ax.boxplot(box_data, labels=['Group 1', 'Group 2', 'Group 3', 'Group 4'],
+                    patch_artist=True, showmeans=True,
+                    boxprops=dict(facecolor='lightblue', edgecolor='black'),
+                    medianprops=dict(color='red', linewidth=2),
+                    meanprops=dict(marker='D', markerfacecolor='green', markersize=8))
+
+    ax.set_xlabel('Groups')
+    ax.set_ylabel('Values')
+    ax.set_title('Box Plot Example')
+    ax.grid(True, axis='y', alpha=0.3, linestyle='--')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_violin_plot(data, ax=None):
+    """Create violin plot showing distribution shapes."""
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
+
+    # Generate multiple distributions
+    violin_data = [np.random.normal(0, std, 100) for std in range(1, 5)]
+
+    parts = ax.violinplot(violin_data, positions=range(1, 5),
+                         showmeans=True, showmedians=True)
+
+    # Customize colors
+    for pc in parts['bodies']:
+        pc.set_facecolor('lightblue')
+        pc.set_alpha(0.7)
+        pc.set_edgecolor('black')
+
+    ax.set_xlabel('Groups')
+    ax.set_ylabel('Values')
+    ax.set_title('Violin Plot Example')
+    ax.set_xticks(range(1, 5))
+    ax.set_xticklabels(['Group 1', 'Group 2', 'Group 3', 'Group 4'])
+    ax.grid(True, axis='y', alpha=0.3, linestyle='--')
+
+    if ax is None:
+        return fig
+    return ax
+
+
+def create_3d_plot():
+    """Create 3D surface plot."""
+    from mpl_toolkits.mplot3d import Axes3D
+
+    fig = plt.figure(figsize=(12, 9))
+    ax = fig.add_subplot(111, projection='3d')
+
+    # Generate data
+    X = np.linspace(-5, 5, 50)
+    Y = np.linspace(-5, 5, 50)
+    X, Y = np.meshgrid(X, Y)
+    Z = np.sin(np.sqrt(X**2 + Y**2))
+
+    # Create surface plot
+    surf = ax.plot_surface(X, Y, Z, cmap='viridis',
+                          edgecolor='none', alpha=0.9)
+
+    # Add colorbar
+    fig.colorbar(surf, ax=ax, shrink=0.5)
+
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    ax.set_title('3D Surface Plot Example')
+
+    # Set viewing angle
+    ax.view_init(elev=30, azim=45)
+
+    plt.tight_layout()
+    return fig
+
+
+def create_comprehensive_figure():
+    """Create a comprehensive figure with multiple subplots."""
+    data = generate_sample_data()
+
+    fig = plt.figure(figsize=(16, 12), constrained_layout=True)
+    gs = GridSpec(3, 3, figure=fig)
+
+    # Create subplots
+    ax1 = fig.add_subplot(gs[0, :2])  # Line plot - top left, spans 2 columns
+    create_line_plot(data, ax1)
+
+    ax2 = fig.add_subplot(gs[0, 2])   # Bar chart - top right
+    create_bar_chart(data, ax2)
+
+    ax3 = fig.add_subplot(gs[1, 0])   # Scatter plot - middle left
+    create_scatter_plot(data, ax3)
+
+    ax4 = fig.add_subplot(gs[1, 1])   # Histogram - middle center
+    create_histogram(data, ax4)
+
+    ax5 = fig.add_subplot(gs[1, 2])   # Box plot - middle right
+    create_box_plot(data, ax5)
+
+    ax6 = fig.add_subplot(gs[2, :2])  # Contour plot - bottom left, spans 2 columns
+    create_contour_plot(data, ax6)
+
+    ax7 = fig.add_subplot(gs[2, 2])   # Heatmap - bottom right
+    create_heatmap(data, ax7)
+
+    fig.suptitle('Comprehensive Matplotlib Template', fontsize=18, fontweight='bold')
+
+    return fig
+
+
+def main():
+    """Main function to run the template."""
+    parser = argparse.ArgumentParser(description='Matplotlib plot template')
+    parser.add_argument('--plot-type', type=str, default='all',
+                       choices=['line', 'scatter', 'bar', 'histogram', 'heatmap',
+                               'contour', 'box', 'violin', '3d', 'all'],
+                       help='Type of plot to create')
+    parser.add_argument('--style', type=str, default='default',
+                       help='Matplotlib style to use')
+    parser.add_argument('--output', type=str, default='plot.png',
+                       help='Output filename')
+
+    args = parser.parse_args()
+
+    # Set style
+    if args.style != 'default':
+        plt.style.use(args.style)
+    else:
+        set_publication_style()
+
+    # Generate data
+    data = generate_sample_data()
+
+    # Create plot based on type
+    plot_functions = {
+        'line': create_line_plot,
+        'scatter': create_scatter_plot,
+        'bar': create_bar_chart,
+        'histogram': create_histogram,
+        'heatmap': create_heatmap,
+        'contour': create_contour_plot,
+        'box': create_box_plot,
+        'violin': create_violin_plot,
+    }
+
+    if args.plot_type == '3d':
+        fig = create_3d_plot()
+    elif args.plot_type == 'all':
+        fig = create_comprehensive_figure()
+    else:
+        fig = plot_functions[args.plot_type](data)
+
+    # Save figure
+    plt.savefig(args.output, dpi=300, bbox_inches='tight')
+    print(f"Plot saved to {args.output}")
+
+    # Display
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
--- a/skills/matplotlib/scripts/style_configurator.py
+++ b/skills/matplotlib/scripts/style_configurator.py
@@ -0,0 +1,409 @@
+#!/usr/bin/env python3
+"""
+Matplotlib Style Configurator
+
+Interactive utility to configure matplotlib style preferences and generate
+custom style sheets. Creates a preview of the style and optionally saves
+it as a .mplstyle file.
+
+Usage:
+    python style_configurator.py [--preset PRESET] [--output FILE] [--preview]
+
+Presets:
+    publication, presentation, web, dark, minimal
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+import argparse
+import os
+
+
+# Predefined style presets
+STYLE_PRESETS = {
+    'publication': {
+        'figure.figsize': (8, 6),
+        'figure.dpi': 100,
+        'savefig.dpi': 300,
+        'savefig.bbox': 'tight',
+        'font.family': 'sans-serif',
+        'font.sans-serif': ['Arial', 'Helvetica'],
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'axes.titlesize': 14,
+        'axes.linewidth': 1.5,
+        'axes.grid': False,
+        'axes.spines.top': False,
+        'axes.spines.right': False,
+        'lines.linewidth': 2,
+        'lines.markersize': 8,
+        'xtick.labelsize': 10,
+        'ytick.labelsize': 10,
+        'xtick.direction': 'in',
+        'ytick.direction': 'in',
+        'xtick.major.size': 6,
+        'ytick.major.size': 6,
+        'xtick.major.width': 1.5,
+        'ytick.major.width': 1.5,
+        'legend.fontsize': 10,
+        'legend.frameon': True,
+        'legend.framealpha': 1.0,
+        'legend.edgecolor': 'black',
+    },
+    'presentation': {
+        'figure.figsize': (12, 8),
+        'figure.dpi': 100,
+        'savefig.dpi': 150,
+        'font.size': 16,
+        'axes.labelsize': 20,
+        'axes.titlesize': 24,
+        'axes.linewidth': 2,
+        'lines.linewidth': 3,
+        'lines.markersize': 12,
+        'xtick.labelsize': 16,
+        'ytick.labelsize': 16,
+        'legend.fontsize': 16,
+        'axes.grid': True,
+        'grid.alpha': 0.3,
+    },
+    'web': {
+        'figure.figsize': (10, 6),
+        'figure.dpi': 96,
+        'savefig.dpi': 150,
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'axes.titlesize': 14,
+        'lines.linewidth': 2,
+        'axes.grid': True,
+        'grid.alpha': 0.2,
+        'grid.linestyle': '--',
+    },
+    'dark': {
+        'figure.facecolor': '#1e1e1e',
+        'figure.edgecolor': '#1e1e1e',
+        'axes.facecolor': '#1e1e1e',
+        'axes.edgecolor': 'white',
+        'axes.labelcolor': 'white',
+        'text.color': 'white',
+        'xtick.color': 'white',
+        'ytick.color': 'white',
+        'grid.color': 'gray',
+        'grid.alpha': 0.3,
+        'axes.grid': True,
+        'legend.facecolor': '#1e1e1e',
+        'legend.edgecolor': 'white',
+        'savefig.facecolor': '#1e1e1e',
+    },
+    'minimal': {
+        'figure.figsize': (10, 6),
+        'axes.spines.top': False,
+        'axes.spines.right': False,
+        'axes.spines.left': False,
+        'axes.spines.bottom': False,
+        'axes.grid': False,
+        'xtick.bottom': True,
+        'ytick.left': True,
+        'axes.axisbelow': True,
+        'lines.linewidth': 2.5,
+        'font.size': 12,
+    }
+}
+
+
+def generate_preview_data():
+    """Generate sample data for style preview."""
+    np.random.seed(42)
+    x = np.linspace(0, 10, 100)
+    y1 = np.sin(x) + 0.1 * np.random.randn(100)
+    y2 = np.cos(x) + 0.1 * np.random.randn(100)
+    scatter_x = np.random.randn(100)
+    scatter_y = 2 * scatter_x + np.random.randn(100)
+    categories = ['A', 'B', 'C', 'D', 'E']
+    bar_values = [25, 40, 30, 55, 45]
+
+    return {
+        'x': x, 'y1': y1, 'y2': y2,
+        'scatter_x': scatter_x, 'scatter_y': scatter_y,
+        'categories': categories, 'bar_values': bar_values
+    }
+
+
+def create_style_preview(style_dict=None):
+    """Create a preview figure demonstrating the style."""
+    if style_dict:
+        plt.rcParams.update(style_dict)
+
+    data = generate_preview_data()
+
+    fig = plt.figure(figsize=(14, 10))
+    gs = GridSpec(2, 2, figure=fig, hspace=0.3, wspace=0.3)
+
+    # Line plot
+    ax1 = fig.add_subplot(gs[0, 0])
+    ax1.plot(data['x'], data['y1'], label='sin(x)', marker='o', markevery=10)
+    ax1.plot(data['x'], data['y2'], label='cos(x)', linestyle='--')
+    ax1.set_xlabel('X axis')
+    ax1.set_ylabel('Y axis')
+    ax1.set_title('Line Plot')
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+
+    # Scatter plot
+    ax2 = fig.add_subplot(gs[0, 1])
+    colors = np.sqrt(data['scatter_x']**2 + data['scatter_y']**2)
+    scatter = ax2.scatter(data['scatter_x'], data['scatter_y'],
+                         c=colors, cmap='viridis', alpha=0.6, s=50)
+    ax2.set_xlabel('X axis')
+    ax2.set_ylabel('Y axis')
+    ax2.set_title('Scatter Plot')
+    cbar = plt.colorbar(scatter, ax=ax2)
+    cbar.set_label('Distance')
+    ax2.grid(True, alpha=0.3)
+
+    # Bar chart
+    ax3 = fig.add_subplot(gs[1, 0])
+    bars = ax3.bar(data['categories'], data['bar_values'],
+                   edgecolor='black', linewidth=1)
+    # Color bars with gradient
+    colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(bars)))
+    for bar, color in zip(bars, colors):
+        bar.set_facecolor(color)
+    ax3.set_xlabel('Categories')
+    ax3.set_ylabel('Values')
+    ax3.set_title('Bar Chart')
+    ax3.grid(True, axis='y', alpha=0.3)
+
+    # Multiple line plot with fills
+    ax4 = fig.add_subplot(gs[1, 1])
+    ax4.plot(data['x'], data['y1'], label='Signal 1', linewidth=2)
+    ax4.fill_between(data['x'], data['y1'] - 0.2, data['y1'] + 0.2,
+                     alpha=0.3, label='±1 std')
+    ax4.plot(data['x'], data['y2'], label='Signal 2', linewidth=2)
+    ax4.fill_between(data['x'], data['y2'] - 0.2, data['y2'] + 0.2,
+                     alpha=0.3)
+    ax4.set_xlabel('X axis')
+    ax4.set_ylabel('Y axis')
+    ax4.set_title('Time Series with Uncertainty')
+    ax4.legend()
+    ax4.grid(True, alpha=0.3)
+
+    fig.suptitle('Style Preview', fontsize=16, fontweight='bold')
+
+    return fig
+
+
+def save_style_file(style_dict, filename):
+    """Save style dictionary as .mplstyle file."""
+    with open(filename, 'w') as f:
+        f.write("# Custom matplotlib style\n")
+        f.write("# Generated by style_configurator.py\n\n")
+
+        # Group settings by category
+        categories = {
+            'Figure': ['figure.'],
+            'Font': ['font.'],
+            'Axes': ['axes.'],
+            'Lines': ['lines.'],
+            'Markers': ['markers.'],
+            'Ticks': ['tick.', 'xtick.', 'ytick.'],
+            'Grid': ['grid.'],
+            'Legend': ['legend.'],
+            'Savefig': ['savefig.'],
+            'Text': ['text.'],
+        }
+
+        for category, prefixes in categories.items():
+            category_items = {k: v for k, v in style_dict.items()
+                            if any(k.startswith(p) for p in prefixes)}
+            if category_items:
+                f.write(f"# {category}\n")
+                for key, value in sorted(category_items.items()):
+                    # Format value appropriately
+                    if isinstance(value, (list, tuple)):
+                        value_str = ', '.join(str(v) for v in value)
+                    elif isinstance(value, bool):
+                        value_str = str(value)
+                    else:
+                        value_str = str(value)
+                    f.write(f"{key}: {value_str}\n")
+                f.write("\n")
+
+    print(f"Style saved to {filename}")
+
+
+def print_style_info(style_dict):
+    """Print information about the style."""
+    print("\n" + "="*60)
+    print("STYLE CONFIGURATION")
+    print("="*60)
+
+    categories = {
+        'Figure Settings': ['figure.'],
+        'Font Settings': ['font.'],
+        'Axes Settings': ['axes.'],
+        'Line Settings': ['lines.'],
+        'Grid Settings': ['grid.'],
+        'Legend Settings': ['legend.'],
+    }
+
+    for category, prefixes in categories.items():
+        category_items = {k: v for k, v in style_dict.items()
+                        if any(k.startswith(p) for p in prefixes)}
+        if category_items:
+            print(f"\n{category}:")
+            for key, value in sorted(category_items.items()):
+                print(f"  {key}: {value}")
+
+    print("\n" + "="*60 + "\n")
+
+
+def list_available_presets():
+    """Print available style presets."""
+    print("\nAvailable style presets:")
+    print("-" * 40)
+    descriptions = {
+        'publication': 'Optimized for academic publications',
+        'presentation': 'Large fonts for presentations',
+        'web': 'Optimized for web display',
+        'dark': 'Dark background theme',
+        'minimal': 'Minimal, clean style',
+    }
+    for preset, desc in descriptions.items():
+        print(f"  {preset:15s} - {desc}")
+    print("-" * 40 + "\n")
+
+
+def interactive_mode():
+    """Run interactive mode to customize style settings."""
+    print("\n" + "="*60)
+    print("MATPLOTLIB STYLE CONFIGURATOR - Interactive Mode")
+    print("="*60)
+
+    list_available_presets()
+
+    preset = input("Choose a preset to start from (or 'custom' for default): ").strip().lower()
+
+    if preset in STYLE_PRESETS:
+        style_dict = STYLE_PRESETS[preset].copy()
+        print(f"\nStarting from '{preset}' preset")
+    else:
+        style_dict = {}
+        print("\nStarting from default matplotlib style")
+
+    print("\nCommon settings you might want to customize:")
+    print("  1. Figure size")
+    print("  2. Font sizes")
+    print("  3. Line widths")
+    print("  4. Grid settings")
+    print("  5. Color scheme")
+    print("  6. Done, show preview")
+
+    while True:
+        choice = input("\nSelect option (1-6): ").strip()
+
+        if choice == '1':
+            width = input("  Figure width (inches, default 10): ").strip() or '10'
+            height = input("  Figure height (inches, default 6): ").strip() or '6'
+            style_dict['figure.figsize'] = (float(width), float(height))
+
+        elif choice == '2':
+            base = input("  Base font size (default 12): ").strip() or '12'
+            style_dict['font.size'] = float(base)
+            style_dict['axes.labelsize'] = float(base) + 2
+            style_dict['axes.titlesize'] = float(base) + 4
+
+        elif choice == '3':
+            lw = input("  Line width (default 2): ").strip() or '2'
+            style_dict['lines.linewidth'] = float(lw)
+
+        elif choice == '4':
+            grid = input("  Enable grid? (y/n): ").strip().lower()
+            style_dict['axes.grid'] = grid == 'y'
+            if style_dict['axes.grid']:
+                alpha = input("  Grid transparency (0-1, default 0.3): ").strip() or '0.3'
+                style_dict['grid.alpha'] = float(alpha)
+
+        elif choice == '5':
+            print("  Theme options: 1=Light, 2=Dark")
+            theme = input("  Select theme (1-2): ").strip()
+            if theme == '2':
+                style_dict.update(STYLE_PRESETS['dark'])
+
+        elif choice == '6':
+            break
+
+    return style_dict
+
+
+def main():
+    """Main function."""
+    parser = argparse.ArgumentParser(
+        description='Matplotlib style configurator',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Show available presets
+  python style_configurator.py --list
+
+  # Preview a preset
+  python style_configurator.py --preset publication --preview
+
+  # Save a preset as .mplstyle file
+  python style_configurator.py --preset publication --output my_style.mplstyle
+
+  # Interactive mode
+  python style_configurator.py --interactive
+        """
+    )
+    parser.add_argument('--preset', type=str, choices=list(STYLE_PRESETS.keys()),
+                       help='Use a predefined style preset')
+    parser.add_argument('--output', type=str,
+                       help='Save style to .mplstyle file')
+    parser.add_argument('--preview', action='store_true',
+                       help='Show style preview')
+    parser.add_argument('--list', action='store_true',
+                       help='List available presets')
+    parser.add_argument('--interactive', action='store_true',
+                       help='Run in interactive mode')
+
+    args = parser.parse_args()
+
+    if args.list:
+        list_available_presets()
+        # Also show currently available matplotlib styles
+        print("\nBuilt-in matplotlib styles:")
+        print("-" * 40)
+        for style in sorted(plt.style.available):
+            print(f"  {style}")
+        return
+
+    if args.interactive:
+        style_dict = interactive_mode()
+    elif args.preset:
+        style_dict = STYLE_PRESETS[args.preset].copy()
+        print(f"Using '{args.preset}' preset")
+    else:
+        print("No preset or interactive mode specified. Showing default preview.")
+        style_dict = {}
+
+    if style_dict:
+        print_style_info(style_dict)
+
+    if args.output:
+        save_style_file(style_dict, args.output)
+
+    if args.preview or args.interactive:
+        print("Creating style preview...")
+        fig = create_style_preview(style_dict if style_dict else None)
+
+        if args.output:
+            preview_filename = args.output.replace('.mplstyle', '_preview.png')
+            plt.savefig(preview_filename, dpi=150, bbox_inches='tight')
+            print(f"Preview saved to {preview_filename}")
+
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()