feat(seaborn): implement cat-strip (#2697)

## Implementation: `cat-strip` - seaborn

Implements the **seaborn** version of `cat-strip`.

**File:** `plots/cat-strip/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20596058931)*

---------

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

Author: github-actions[bot]

plots/cat-strip/implementations/seaborn.py

@@ -0,0 +1,64 @@
+""" pyplots.ai
+cat-strip: Categorical Strip 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 quality scores across manufacturing lines
+np.random.seed(42)
+
+categories = ["Line A", "Line B", "Line C", "Line D", "Line E"]
+n_per_category = 25
+
+data = []
+for cat in categories:
+    if cat == "Line A":
+        values = np.random.normal(85, 5, n_per_category)
+    elif cat == "Line B":
+        values = np.random.normal(78, 8, n_per_category)
+    elif cat == "Line C":
+        values = np.random.normal(92, 3, n_per_category)
+    elif cat == "Line D":
+        values = np.random.normal(75, 10, n_per_category)
+    else:
+        values = np.random.normal(88, 6, n_per_category)
+
+    for v in values:
+        data.append({"Manufacturing Line": cat, "Quality Score": v})
+
+df = pd.DataFrame(data)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+sns.stripplot(
+    data=df,
+    x="Manufacturing Line",
+    y="Quality Score",
+    hue="Manufacturing Line",
+    palette=["#306998", "#FFD43B", "#4B8BBE", "#646464", "#F0DB4F"],
+    size=12,
+    jitter=0.25,
+    alpha=0.8,
+    ax=ax,
+    legend=False,
+)
+
+# Style
+ax.set_title("cat-strip · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+ax.set_xlabel("Manufacturing Line", fontsize=20)
+ax.set_ylabel("Quality Score", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, axis="y", alpha=0.3, linestyle="--")
+
+# Set y-axis range to show full data
+ax.set_ylim(45, 105)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/cat-strip/metadata/seaborn.yaml

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+library: seaborn
+specification_id: cat-strip
+created: '2025-12-30T12:04:59Z'
+updated: '2025-12-30T12:07:53Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20596058931
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/cat-strip/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/cat-strip/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of seaborn modern API with hue parameter to avoid deprecation warnings
+  - Clear visual differentiation between manufacturing lines with distinct colors
+  - Data demonstrates variety in distributions (tight clustering in Line C vs wide
+    spread in Line D)
+  - Professional title format and appropriate font sizing throughout
+  - Good use of jitter (0.25) to show individual points without excessive spread
+  weaknesses:
+  - Axis labels lack units (Quality Score could specify scale like 0-100)
+  - Two yellow shades (Line B, Line E) are somewhat similar - could use more distinct
+    palette like colorblind
+  - Does not showcase advanced seaborn features like overlaying with boxplot or using
+    built-in palettes
+  - Some data points exceed 100 which is unusual for a score metric