feat(seaborn): implement point-basic (#2566)

## Implementation: `point-basic` - seaborn

Implements the **seaborn** version of `point-basic`.

**File:** `plots/point-basic/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593348794)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/point-basic/implementations/seaborn.py

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+""" pyplots.ai
+point-basic: Point Estimate Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data: Product satisfaction ratings with confidence intervals
+np.random.seed(42)
+categories = ["Product A", "Product B", "Product C", "Product D", "Product E", "Product F"]
+estimates = [7.2, 6.5, 8.1, 5.9, 7.8, 6.2]
+# Varying confidence interval widths to show different uncertainty levels
+ci_widths = [0.8, 1.2, 0.5, 1.5, 0.7, 1.1]
+lower = [e - w for e, w in zip(estimates, ci_widths, strict=True)]
+upper = [e + w for e, w in zip(estimates, ci_widths, strict=True)]
+
+df = pd.DataFrame({"Product": categories, "Satisfaction": estimates, "Lower": lower, "Upper": upper})
+
+# Create plot (4800x2700 px at 300 dpi = 16x9 inches)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot points using seaborn pointplot
+# Using horizontal orientation for better label readability
+sns.pointplot(
+    data=df,
+    x="Satisfaction",
+    y="Product",
+    orient="h",
+    color="#306998",
+    markers="o",
+    markersize=15,
+    linestyle="none",  # Remove connecting lines between points
+    err_kws={"linewidth": 0},  # We'll draw custom error bars for more control
+    ax=ax,
+)
+
+# Draw error bars with caps manually for better control
+for i, (_, row) in enumerate(df.iterrows()):
+    ax.errorbar(
+        x=row["Satisfaction"],
+        y=i,
+        xerr=[[row["Satisfaction"] - row["Lower"]], [row["Upper"] - row["Satisfaction"]]],
+        fmt="none",
+        color="#306998",
+        capsize=8,
+        capthick=3,
+        elinewidth=3,
+    )
+
+# Add reference line at overall mean
+overall_mean = np.mean(estimates)
+ax.axvline(
+    x=overall_mean,
+    color="#FFD43B",
+    linestyle="--",
+    linewidth=2.5,
+    alpha=0.8,
+    label=f"Overall Mean ({overall_mean:.1f})",
+)
+
+# Labels and styling (scaled for 4800x2700)
+ax.set_xlabel("Satisfaction Score (1-10)", fontsize=20)
+ax.set_ylabel("Product", fontsize=20)
+ax.set_title("point-basic · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--", axis="x")
+
+# Legend
+ax.legend(fontsize=16, loc="lower right")
+
+# Set x-axis limits to show context
+ax.set_xlim(3, 10)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/point-basic/metadata/seaborn.yaml

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+library: seaborn
+specification_id: point-basic
+created: '2025-12-30T09:31:12Z'
+updated: '2025-12-30T09:40:23Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593348794
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/point-basic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/point-basic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent horizontal orientation for category label readability as recommended
+    in spec
+  - Clear visual hierarchy with well-sized points and error bars with caps
+  - Helpful reference line showing overall mean with legend explanation
+  - Good use of varying confidence interval widths to demonstrate uncertainty differences
+  - Proper title format and axis labeling with units
+  weaknesses:
+  - Error bars drawn manually with matplotlib errorbar instead of using seaborn native
+    confidence interval features
+  - All confidence intervals are symmetric around estimates; real-world CIs are often
+    asymmetric