feat(seaborn): implement contour-density (#2572)

## Implementation: `contour-density` - seaborn

Implements the **seaborn** version of `contour-density`.

**File:** `plots/contour-density/implementations/seaborn.py`

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593350753)*

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

Author: github-actions[bot]

plots/contour-density/implementations/seaborn.py

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+""" pyplots.ai
+contour-density: Density Contour Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# Data - bivariate normal distributions with two clusters
+np.random.seed(42)
+
+# Main cluster
+n1 = 300
+x1 = np.random.normal(loc=5, scale=1.5, size=n1)
+y1 = np.random.normal(loc=5, scale=1.5, size=n1)
+
+# Secondary cluster
+n2 = 150
+x2 = np.random.normal(loc=9, scale=1.0, size=n2)
+y2 = np.random.normal(loc=8, scale=1.0, size=n2)
+
+# Combine clusters
+x = np.concatenate([x1, x2])
+y = np.concatenate([y1, y2])
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Density contour plot using seaborn's kdeplot (filled)
+sns.kdeplot(x=x, y=y, ax=ax, levels=10, fill=True, cmap="viridis", alpha=0.8)
+
+# Add contour lines for clarity
+sns.kdeplot(x=x, y=y, ax=ax, levels=10, color="#306998", linewidths=1.5, alpha=0.7)
+
+# Scatter plot overlay for context (small, semi-transparent points)
+ax.scatter(x, y, s=15, color="white", alpha=0.3, edgecolors="none")
+
+# Styling
+ax.set_xlabel("X Variable (units)", fontsize=20)
+ax.set_ylabel("Y Variable (units)", fontsize=20)
+ax.set_title("contour-density · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/contour-density/metadata/seaborn.yaml

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+library: seaborn
+specification_id: contour-density
+created: '2025-12-30T09:31:30Z'
+updated: '2025-12-30T09:41:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593350753
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-density/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-density/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent use of seaborn kdeplot for 2D kernel density estimation with both filled
+    and line contours
+  - Two-cluster data effectively demonstrates density visualization capabilities
+  - Viridis colormap provides excellent colorblind accessibility and visual appeal
+  - Scatter overlay adds context without cluttering the visualization
+  - Clean, well-structured code following KISS principles
+  weaknesses:
+  - No colorbar/legend to show the density scale mapping
+  - Generic axis labels (X Variable, Y Variable) rather than a real-world scenario