feat(seaborn): implement violin-box (#2666)

## Implementation: `violin-box` - seaborn

Implements the **seaborn** version of `violin-box`.

**File:** `plots/violin-box/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595338490)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/violin-box/implementations/seaborn.py

@@ -0,0 +1,58 @@
+""" pyplots.ai
+violin-box: Violin Plot with Embedded Box Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 95/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - Product quality scores across manufacturing batches
+np.random.seed(42)
+
+# Create varied distributions for each batch to show different shapes
+batch_a = np.random.normal(75, 8, 120)  # Normal, centered around 75
+batch_b = np.concatenate([np.random.normal(60, 5, 60), np.random.normal(80, 5, 60)])  # Bimodal
+batch_c = np.random.exponential(10, 120) + 50  # Right-skewed
+batch_d = 95 - np.random.exponential(10, 120)  # Left-skewed
+
+# Combine into DataFrame
+df = pd.DataFrame(
+    {
+        "Quality Score": np.concatenate([batch_a, batch_b, batch_c, batch_d]),
+        "Batch": ["Batch A"] * 120 + ["Batch B"] * 120 + ["Batch C"] * 120 + ["Batch D"] * 120,
+    }
+)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Create violin plot with embedded box plot using inner='box'
+sns.violinplot(
+    data=df,
+    x="Batch",
+    y="Quality Score",
+    hue="Batch",
+    palette=["#306998", "#FFD43B", "#4A90A4", "#E8A838"],
+    inner="box",
+    linewidth=2,
+    saturation=0.9,
+    legend=False,
+    ax=ax,
+)
+
+# Styling
+ax.set_title("violin-box · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+ax.set_xlabel("Manufacturing Batch", fontsize=20)
+ax.set_ylabel("Quality Score (0-100)", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# Set y-axis limits for better visibility
+ax.set_ylim(30, 100)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/violin-box/metadata/seaborn.yaml

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+library: seaborn
+specification_id: violin-box
+created: '2025-12-30T11:22:35Z'
+updated: '2025-12-30T11:31:26Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595338490
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-box/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-box/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 95
+review:
+  strengths:
+  - Excellent data design showing four distinct distribution shapes (normal, bimodal,
+    right-skewed, left-skewed) that perfectly demonstrate the value of violin plots
+  - Clean use of seaborn inner=box parameter for embedded box plots
+  - Modern API usage with hue parameter to avoid deprecation warnings
+  - Professional color palette with good contrast and accessibility
+  - Appropriate font sizes and clear labeling with units
+  weaknesses:
+  - Grid only on y-axis, though acceptable for categorical x-axis
+  - Legend disabled which is fine since batch names are on x-axis, but a small legend
+    could enhance standalone viewing