diff --git a/plots/heatmap-basic/implementations/plotnine.py b/plots/heatmap-basic/implementations/plotnine.py
index f28feba654..d3cc860b10 100644
--- a/plots/heatmap-basic/implementations/plotnine.py
+++ b/plots/heatmap-basic/implementations/plotnine.py
@@ -1,7 +1,7 @@
 """ pyplots.ai
 heatmap-basic: Basic Heatmap
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 92/100 | Updated: 2026-02-16
 """
 
 import numpy as np
@@ -9,67 +9,90 @@
 from plotnine import (
     aes,
     element_blank,
+    element_rect,
     element_text,
     geom_text,
     geom_tile,
     ggplot,
     labs,
+    scale_color_identity,
     scale_fill_gradient2,
+    scale_x_discrete,
+    scale_y_discrete,
     theme,
     theme_minimal,
 )
 
 
-# Data - 8x8 matrix with meaningful patterns (performance metrics by region and quarter)
+# Data - 8x8 matrix: quarterly growth rates (%) by department
 np.random.seed(42)
-rows = ["Region A", "Region B", "Region C", "Region D", "Region E", "Region F", "Region G", "Region H"]
-cols = ["Q1 2023", "Q2 2023", "Q3 2023", "Q4 2023", "Q1 2024", "Q2 2024", "Q3 2024", "Q4 2024"]
+departments = ["Engineering", "Marketing", "Sales", "Finance", "Operations", "HR", "Research", "Support"]
+quarters = ["Q1 '23", "Q2 '23", "Q3 '23", "Q4 '23", "Q1 '24", "Q2 '24", "Q3 '24", "Q4 '24"]
 
-# Create data with a trend and variation
-base_values = np.linspace(-30, 40, 8)  # Trend across columns
-row_effects = np.random.uniform(-10, 10, 8)  # Row-specific offsets
-values = np.zeros((8, 8))
-for i in range(8):
-    for j in range(8):
-        values[i, j] = base_values[j] + row_effects[i] + np.random.uniform(-8, 8)
+# Vectorized growth rates with recovery trend and departmental variation
+base_trend = np.linspace(-18, 22, 8)
+dept_offsets = np.array([-6, 10, 14, -3, 4, -10, 8, -5])
+values = np.round(base_trend[np.newaxis, :] + dept_offsets[:, np.newaxis] + np.random.normal(0, 4, (8, 8)), 1)
 
-# Create long-form DataFrame for plotnine
-data = []
-for i, row in enumerate(rows):
-    for j, col in enumerate(cols):
-        data.append({"x": col, "y": row, "value": round(values[i, j], 1)})
+# Inject distinctive extreme values for storytelling focal points
+values[5, 0] = -32.5  # HR deep crisis in Q1 '23
+values[2, 7] = 38.2  # Sales strong recovery in Q4 '24
+values[6, 6] = 33.7  # Research surge in Q3 '24
 
-df = pd.DataFrame(data)
+# Build long-form DataFrame via meshgrid indexing
+dept_idx, qtr_idx = np.meshgrid(np.arange(8), np.arange(8), indexing="ij")
+df = pd.DataFrame(
+    {
+        "Department": pd.Categorical(
+            [departments[i] for i in dept_idx.ravel()], categories=departments[::-1], ordered=True
+        ),
+        "Quarter": pd.Categorical([quarters[j] for j in qtr_idx.ravel()], categories=quarters, ordered=True),
+        "Growth (%)": values.ravel(),
+    }
+)
+
+# Conditional text color: white on dark blue, dark gray on mid, dark brown on gold
+df["text_color"] = np.where(df["Growth (%)"] < -12, "white", np.where(df["Growth (%)"] < 18, "#3a3a3a", "#4a2e00"))
 
-# Preserve ordering
-df["x"] = pd.Categorical(df["x"], categories=cols, ordered=True)
-df["y"] = pd.Categorical(df["y"], categories=rows[::-1], ordered=True)  # Reverse for top-to-bottom
+# Signed annotation labels
+df["label"] = [f"{v:+.1f}" for v in df["Growth (%)"]]
 
 # Plot
 plot = (
-    ggplot(df, aes(x="x", y="y", fill="value"))
-    + geom_tile(color="white", size=0.5)
-    + geom_text(aes(label="value"), size=12, color="black")
+    ggplot(df, aes(x="Quarter", y="Department"))
+    + geom_tile(aes(fill="Growth (%)"), color="white", size=1.2)
+    + geom_text(aes(label="label", color="text_color"), size=10, fontweight="bold", show_legend=False)
     + scale_fill_gradient2(
-        low="#306998",  # Python Blue for negative
-        mid="white",
-        high="#FFD43B",  # Python Yellow for positive
-        midpoint=0,
-        name="Value",
+        low="#14405e", mid="#ede8e3", high="#c47d00", midpoint=0, name="Growth (%)", limits=(-35, 40)
+    )
+    + scale_color_identity()
+    + scale_x_discrete(expand=(0, 0.5))
+    + scale_y_discrete(expand=(0, 0.5))
+    + labs(
+        x="Quarter",
+        y="Department",
+        title="Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai",
+        subtitle="Year-over-year growth rate (%) across departments, Q1 2023 – Q4 2024",
     )
-    + labs(x="Time Period", y="Region", title="heatmap-basic · plotnine · pyplots.ai")
     + theme_minimal()
     + theme(
         figure_size=(16, 9),
-        text=element_text(size=14),
-        axis_title=element_text(size=20),
-        axis_text_x=element_text(size=14, rotation=45, ha="right"),
-        axis_text_y=element_text(size=16),
-        plot_title=element_text(size=24),
-        legend_title=element_text(size=16),
+        text=element_text(family="sans-serif"),
+        plot_title=element_text(size=24, ha="center", weight="bold", margin={"b": 2}),
+        plot_subtitle=element_text(size=16, ha="center", color="#555555", margin={"b": 8}),
+        axis_title_x=element_text(size=20, margin={"t": 10}),
+        axis_title_y=element_text(size=20, margin={"r": 8}),
+        axis_text_x=element_text(size=16, rotation=45, ha="right", margin={"t": 4}),
+        axis_text_y=element_text(size=16, ha="right", margin={"r": 4}),
+        legend_title=element_text(size=16, weight="bold"),
         legend_text=element_text(size=14),
+        legend_position="right",
+        legend_key_height=40,
         panel_grid_major=element_blank(),
         panel_grid_minor=element_blank(),
+        panel_background=element_rect(fill="white", color="none"),
+        plot_background=element_rect(fill="#f7f7f7", color="none"),
+        plot_margin=0.02,
     )
 )
 
diff --git a/plots/heatmap-basic/metadata/plotnine.yaml b/plots/heatmap-basic/metadata/plotnine.yaml
index 9263e7671c..d1098bff26 100644
--- a/plots/heatmap-basic/metadata/plotnine.yaml
+++ b/plots/heatmap-basic/metadata/plotnine.yaml
@@ -1,12 +1,12 @@
 library: plotnine
 specification_id: heatmap-basic
 created: '2025-12-23T00:47:36Z'
-updated: '2025-12-23T00:51:16Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-16T20:34:16Z'
+generated_by: claude-opus-4-6
 workflow_run: 20447967616
 issue: 0
-python_version: 3.13.11
-library_version: 0.15.2
+python_version: 3.14.3
+library_version: 0.15.3
 preview_url: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/plotnine/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/plotnine/plot_thumb.png
 preview_html: null
@@ -14,159 +14,168 @@ quality_score: 92
 impl_tags:
   dependencies: []
   techniques:
-  - colorbar
   - annotations
   - layer-composition
   patterns:
   - data-generation
-  - wide-to-long
+  - matrix-construction
   dataprep: []
   styling:
+  - custom-colormap
   - edge-highlighting
 review:
   strengths:
-  - Excellent diverging color scheme using Python brand colors (blue/yellow) that
-    is colorblind-safe
-  - Clear value annotations in each cell make exact values easy to read
-  - Well-structured data with meaningful trend pattern showing performance improvement
-    over time
-  - Clean use of plotnine grammar of graphics with proper layering (geom_tile + geom_text)
-  - Correct categorical ordering preserves temporal sequence on x-axis and intuitive
-    top-to-bottom region ordering
-  - Title follows exact specification format
+  - Excellent diverging color palette (steel blue to cream to gold) with well-chosen
+    midpoint at 0, creating an intuitive visual encoding of positive/negative growth
+  - Conditional text color logic (white on dark, dark gray on mid, dark brown on gold)
+    ensures all annotations are readable against their cell background
+  - Data tells a compelling recovery story with intentional extreme values (HR -32.5
+    crisis, Sales +38.2 surge) serving as visual focal points
+  - Clean, idiomatic plotnine code with vectorized data generation and meshgrid-based
+    long-form DataFrame construction
+  - 'Fully polished theme: removed grid lines, custom background, generous margins,
+    rotated tick labels'
   weaknesses:
-  - Axis labels lack units or additional context (e.g., Value could specify Performance
-    Index or similar)
-  - Cell annotation text color is always black, which reduces contrast on darker blue
-    cells (e.g., Region G, Q1 2023 at -45.4)
-  image_description: The heatmap displays an 8x8 matrix showing performance metrics
-    across 8 regions (A-H on the y-axis) and 8 time periods (Q1 2023 through Q4 2024
-    on the x-axis). The color scheme uses a diverging palette with Python Blue (#306998)
-    for negative values and Python Yellow (#FFD43B) for positive values, with white
-    at the midpoint (0). Each cell contains its numerical value as a text annotation.
-    The colorbar legend on the right shows the "Value" scale ranging from approximately
-    -25 to +25. The title "heatmap-basic · plotnine · pyplots.ai" appears at the top.
-    The plot clearly shows a trend pattern where earlier quarters have predominantly
-    negative values (blue) and later quarters show positive values (yellow), demonstrating
-    regional performance improvement over time.
+  - The colorbar legend could benefit from more tick marks to better communicate the
+    full range of the scale
+  - Minor wasted horizontal space on the right side between the plot and the legend
+    area
+  image_description: 'The heatmap displays an 8x8 grid of quarterly growth rates (%)
+    across 8 departments (Engineering, Marketing, Sales, Finance, Operations, HR,
+    Research, Support) over 8 quarters (Q1 ''23 through Q4 ''24). A diverging color
+    scheme is used: dark steel blue for strongly negative values, cream/beige for
+    near-zero values, and gold/amber for strongly positive values. Each cell contains
+    a bold, signed numeric annotation (e.g., "+38.2", "-32.5"). Text color adapts
+    to background: white on dark blue cells, dark gray on mid-range cells, and dark
+    brown on gold cells. The title "Quarterly Growth by Department · heatmap-basic
+    · plotnine · pyplots.ai" is centered and bold at the top, with a subtitle describing
+    the data. X-axis labels are rotated 45 degrees showing quarter names, Y-axis shows
+    department names. A vertical colorbar legend labeled "Growth (%)" appears on the
+    right with a range from approximately -20 to 40. White borders separate cells
+    cleanly. The background is light gray (#f7f7f7). The plot clearly shows a recovery
+    narrative from left (negative) to right (positive).'
   criteria_checklist:
     visual_quality:
-      score: 37
-      max: 40
+      score: 28
+      max: 30
       items:
       - id: VQ-01
         name: Text Legibility
-        score: 10
-        max: 10
+        score: 8
+        max: 8
         passed: true
-        comment: Title at 24pt, axis labels at 20pt, tick labels at 14-16pt, cell
-          annotations at size 12 - all clearly readable
+        comment: 'All font sizes explicitly set: title 24pt, axis labels 20pt, ticks
+          16pt, annotations bold size=10'
       - id: VQ-02
         name: No Overlap
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: No overlapping text elements, x-axis labels rotated 45° to avoid
-          collision
+        comment: No overlapping text anywhere, rotated x-axis labels, well-spaced
+          annotations
       - id: VQ-03
         name: Element Visibility
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: Tiles are well-sized with clear white borders, annotations visible
-          on all backgrounds
+        comment: Tiles fill grid perfectly, annotations clearly visible in every cell
       - id: VQ-04
         name: Color Accessibility
-        score: 5
-        max: 5
+        score: 3
+        max: 4
         passed: true
-        comment: Blue-white-yellow diverging palette is colorblind-safe (avoids red-green)
+        comment: Diverging blue-cream-gold avoids red-green issues; not a standard
+          perceptually-uniform palette
       - id: VQ-05
         name: Layout Balance
-        score: 4
-        max: 5
+        score: 3
+        max: 4
         passed: true
-        comment: Good proportions, slight white space on right side near colorbar
-          but acceptable
+        comment: Good canvas utilization, minor wasted space near legend on right
       - id: VQ-06
-        name: Axis Labels
-        score: 1
+        name: Axis Labels & Title
+        score: 2
         max: 2
         passed: true
-        comment: '"Time Period" and "Region" are descriptive but lack units'
-      - id: VQ-07
-        name: Grid & Legend
-        score: 1
-        max: 2
+        comment: Descriptive labels with units in annotations and legend
+    design_excellence:
+      score: 16
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 7
+        max: 8
+        passed: true
+        comment: Custom diverging palette, conditional text colors, white cell borders,
+          light gray background — publication-quality
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
         passed: true
-        comment: 'Grid removed for clean heatmap look, legend well placed; minor:
-          panel_grid_major=element_blank removes subtle structure'
+        comment: Grid removed, custom backgrounds, generous whitespace, spines absent
+          — highly polished
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Recovery narrative with extreme focal points, conditional text color
+          guides attention
     spec_compliance:
-      score: 25
-      max: 25
+      score: 15
+      max: 15
       items:
       - id: SC-01
         name: Plot Type
-        score: 8
-        max: 8
+        score: 5
+        max: 5
         passed: true
         comment: Correct heatmap using geom_tile
       - id: SC-02
-        name: Data Mapping
-        score: 5
-        max: 5
+        name: Required Features
+        score: 4
+        max: 4
         passed: true
-        comment: X=time periods, Y=regions, fill=value correctly assigned
+        comment: 'All spec features present: diverging colormap, annotations, colorbar,
+          logical ordering'
       - id: SC-03
-        name: Required Features
-        score: 5
-        max: 5
+        name: Data Mapping
+        score: 3
+        max: 3
         passed: true
-        comment: Diverging colormap for +/- values ✓, value annotations ✓, colorbar
-          legend
+        comment: X=Quarter, Y=Department, fill=Growth (%) — correct
       - id: SC-04
-        name: Data Range
+        name: Title & Legend
         score: 3
         max: 3
         passed: true
-        comment: All data visible, axes show complete range
-      - id: SC-05
-        name: Legend Accuracy
-        score: 2
-        max: 2
-        passed: true
-        comment: '"Value" legend label correct with proper scale'
-      - id: SC-06
-        name: Title Format
-        score: 2
-        max: 2
-        passed: true
-        comment: Uses exact format "heatmap-basic · plotnine · pyplots.ai"
+        comment: Title contains heatmap-basic · plotnine · pyplots.ai, legend labeled
+          Growth (%)
     data_quality:
-      score: 18
-      max: 20
+      score: 14
+      max: 15
       items:
       - id: DQ-01
         name: Feature Coverage
-        score: 7
-        max: 8
+        score: 5
+        max: 6
         passed: true
-        comment: Shows both positive and negative values, trend patterns, regional
-          variation; could show more extreme outliers
+        comment: Shows positive/negative, near-zero, extremes, variation across both
+          dimensions
       - id: DQ-02
         name: Realistic Context
-        score: 7
-        max: 7
+        score: 5
+        max: 5
         passed: true
-        comment: Performance metrics by region and quarter is a real, comprehensible
-          business scenario
+        comment: 'Realistic business scenario: quarterly departmental growth rates'
       - id: DQ-03
         name: Appropriate Scale
         score: 4
-        max: 5
+        max: 4
         passed: true
-        comment: Values range from ~-45 to ~+47, plausible for performance metrics
-          but units undefined
+        comment: Growth rates -32.5% to +38.2% are plausible
     code_quality:
       score: 10
       max: 10
@@ -176,41 +185,47 @@ review:
         score: 3
         max: 3
         passed: true
-        comment: Clean imports → data → plot → save structure, no unnecessary functions/classes
+        comment: Clean imports → data → plot → save, no functions/classes
       - id: CQ-02
         name: Reproducibility
-        score: 3
-        max: 3
+        score: 2
+        max: 2
         passed: true
-        comment: Uses np.random.seed(42)
+        comment: np.random.seed(42)
       - id: CQ-03
         name: Clean Imports
         score: 2
         max: 2
         passed: true
-        comment: All imports are used
+        comment: All imports used
       - id: CQ-04
-        name: No Deprecated API
-        score: 1
-        max: 1
+        name: Code Elegance
+        score: 2
+        max: 2
         passed: true
-        comment: Uses current plotnine API
+        comment: Well-organized, vectorized data generation, appropriate complexity
       - id: CQ-05
-        name: Output Correct
+        name: Output & API
         score: 1
         max: 1
         passed: true
-        comment: Saves as plot.png
-    library_features:
-      score: 2
-      max: 5
+        comment: Saves as plot.png, no deprecated functions
+    library_mastery:
+      score: 9
+      max: 10
       items:
-      - id: LF-01
-        name: Uses distinctive library features
-        score: 2
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 5
         max: 5
-        passed: false
-        comment: Good use of ggplot grammar with geom_tile + geom_text layering, but
-          scale_fill_gradient2 is basic; could leverage faceting or more advanced
-          theming
+        passed: true
+        comment: 'Idiomatic grammar of graphics: ggplot + aes + geom_tile + geom_text
+          + scales + theme'
+      - id: LM-02
+        name: Distinctive Features
+        score: 4
+        max: 5
+        passed: true
+        comment: scale_fill_gradient2 with midpoint, scale_color_identity for conditional
+          text, pd.Categorical ordering
   verdict: APPROVED