feat(seaborn): implement line-basic

Add basic line plot implementation for seaborn library:
- plots/seaborn/lineplot/line-basic/default.py
- specs/line-basic.md

Features:
- Time series and trend visualization
- Customizable line styles, markers, and colors
- Data validation and type checking
- Optional data sorting for proper line rendering
- Follows project code quality standards

Author: github-actions[bot]

plots/seaborn/lineplot/line-basic/default.py

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+"""
+line-basic: Basic Line Plot
+Implementation for: seaborn
+Variant: default
+Python: 3.10+
+"""
+
+from typing import TYPE_CHECKING, Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    x: str,
+    y: str,
+    figsize: tuple[float, float] = (16, 9),
+    color: str = "steelblue",
+    linewidth: float = 2.0,
+    linestyle: str = "-",
+    marker: Optional[str] = None,
+    markersize: float = 6,
+    alpha: float = 1.0,
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    sort_data: bool = True,
+    **kwargs,
+) -> "Figure":
+    """
+    Create a basic line plot visualizing trends over a continuous or sequential axis.
+
+    Args:
+        data: Input DataFrame with required columns
+        x: Column name for x-axis values
+        y: Column name for y-axis values
+        figsize: Figure size as (width, height) tuple (default: (16, 9))
+        color: Line color (default: "steelblue")
+        linewidth: Width of the line (default: 2.0)
+        linestyle: Line style, e.g., '-', '--', '-.', ':' (default: '-')
+        marker: Marker style for data points (default: None)
+        markersize: Size of markers (default: 6)
+        alpha: Transparency level (default: 1.0)
+        title: Plot title (default: None)
+        xlabel: X-axis label (default: uses column name)
+        ylabel: Y-axis label (default: uses column name)
+        sort_data: Whether to sort data by x-axis values (default: True)
+        **kwargs: Additional parameters passed to seaborn lineplot function
+
+    Returns:
+        Matplotlib Figure object
+
+    Raises:
+        ValueError: If data is empty
+        KeyError: If required columns not found
+        TypeError: If y column contains non-numeric data
+
+    Example:
+        >>> data = pd.DataFrame({'time': [1, 2, 3], 'value': [2, 4, 3]})
+        >>> fig = create_plot(data, 'time', 'value')
+    """
+    # Input validation
+    if data.empty:
+        raise ValueError("Data cannot be empty")
+
+    # Check required columns
+    for col in [x, y]:
+        if col not in data.columns:
+            available = ", ".join(data.columns)
+            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
+
+    # Check if y column is numeric
+    if not pd.api.types.is_numeric_dtype(data[y]):
+        raise TypeError(f"Column '{y}' must contain numeric data")
+
+    # Create a copy to avoid modifying original data
+    plot_data = data.copy()
+
+    # Sort data by x-axis for proper line rendering
+    if sort_data:
+        plot_data = plot_data.sort_values(by=x).reset_index(drop=True)
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # Set seaborn style for clean appearance
+    sns.set_style("whitegrid")
+
+    # Plot data using seaborn lineplot
+    sns.lineplot(
+        data=plot_data,
+        x=x,
+        y=y,
+        color=color,
+        linewidth=linewidth,
+        linestyle=linestyle,
+        marker=marker,
+        markersize=markersize,
+        alpha=alpha,
+        ax=ax,
+        **kwargs,
+    )
+
+    # Labels and title
+    ax.set_xlabel(xlabel or x)
+    ax.set_ylabel(ylabel or y)
+
+    if title:
+        ax.set_title(title)
+
+    # Apply styling - make grid subtle
+    ax.grid(True, alpha=0.3)
+
+    # Reset seaborn style to avoid affecting other plots
+    sns.set_style("ticks")
+
+    # Layout
+    plt.tight_layout()
+
+    return fig
+
+
+if __name__ == "__main__":
+    # Sample data for testing - time series data
+    np.random.seed(42)
+    n_points = 50
+
+    # Generate time series data with trend and noise
+    time = np.arange(n_points)
+    values = 10 + 0.5 * time + np.random.randn(n_points) * 2
+
+    data = pd.DataFrame({"time": time, "value": values})
+
+    # Create plot
+    fig = create_plot(
+        data, "time", "value", title="Basic Line Plot Example", xlabel="Time", ylabel="Value", marker="o", markersize=4
+    )
+
+    # Save for inspection - ALWAYS use 'plot.png' as filename
+    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+    print("Plot saved to plot.png")

specs/line-basic.md

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+# line-basic: Basic Line Plot
+
+A fundamental line plot that visualizes trends and changes in data over a continuous or sequential axis, commonly used for time series and ordered data.
+
+## Data Requirements
+
+- **x**: Column for x-axis values (numeric, datetime, or ordered categorical)
+- **y**: Numeric column for y-axis values
+
+## Optional Parameters
+
+- `figsize`: Figure size as (width, height) tuple (default: (16, 9))
+- `color`: Line color (default: "steelblue")
+- `linewidth`: Width of the line (default: 2.0)
+- `linestyle`: Line style, e.g., '-', '--', '-.', ':' (default: '-')
+- `marker`: Marker style for data points (default: None)
+- `markersize`: Size of markers (default: 6)
+- `alpha`: Transparency level (default: 1.0)
+- `title`: Plot title (default: None)
+- `xlabel`: X-axis label (default: uses column name)
+- `ylabel`: Y-axis label (default: uses column name)
+
+## Expected Output
+
+A line plot with:
+- X and Y axes labeled with column names (or custom labels)
+- Smooth, continuous line connecting data points
+- Grid visible but subtle (alpha ≤ 0.3)
+- Professional appearance with proper spacing
+- Optional markers at data points for clarity
+
+## Quality Criteria
+
+- [x] Axes labeled clearly
+- [x] Grid visible but subtle
+- [x] Line clearly visible with appropriate width
+- [x] No overlapping labels
+- [x] Appropriate figure size (16:9 aspect ratio)
+- [x] Type hints and validation present
+- [x] Data sorted by x-axis for proper line rendering
+
+## Examples
+
+### Example 1: Basic Usage
+```python
+import pandas as pd
+data = pd.DataFrame({
+    'time': [1, 2, 3, 4, 5],
+    'value': [2, 4, 3, 5, 6]
+})
+fig = create_plot(data, 'time', 'value')
+```
+
+### Example 2: Custom Styling
+```python
+fig = create_plot(
+    data,
+    'time',
+    'value',
+    color='darkblue',
+    linewidth=2.5,
+    marker='o',
+    title='Trend Analysis'
+)
+```
+
+## Implementation Notes
+
+- Data should be sorted by x-axis values for proper line rendering
+- Handle missing/NaN values gracefully
+- Validate that y column contains numeric data
+- X-axis can be numeric, datetime, or ordered categorical
+
+## Tags
+
+line, trend, time-series, basic, 2d
+
+## Use Cases
+
+- Time series visualization of stock prices
+- Tracking metrics over time (e.g., website traffic)
+- Displaying trends in scientific measurements
+- Monitoring system performance metrics
+- Visualizing growth or decline patterns