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name: presentation-datavis description: > Use when a presentation needs charts, tables, data visualizations, or data-driven slides — tagged [CHART] slides from narrative, "visualize this data", or any data-to-slide conversion. Part of the presentation-* skill family. triggers:

• data visualization for presentations
• charts for slides
• "visualize this data"
• tagged [CHART] slides from narrative
• data-driven presentation
• chart generation
• slide charts and graphs family: presentation disambiguation: QUANTITATIVE visuals — charts, tables, data-driven slides. Architecture, flow and sequence visuals are presentation-diagrams.

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Presentation Data Visualization

Child of presentation-builder. This skill handles all chart, table, and data visualization generation for presentation slides. It receives tagged [CHART] placeholders from the narrative skill and produces embeddable visual assets.

Siblings: presentation-narrative (story and slide structure), presentation-renderer (final output assembly), presentation-builder (parent orchestrator).

When NOT to Use

• Standalone data analysis without a presentation context — use general Python data tools instead.
• Infographics or illustrations that are not data-driven — use vertex-banana for image generation.
• Simple text tables in markdown that do not need visual rendering — just write the markdown directly.
• Dashboard or web app visualizations — this skill targets static slide assets, not interactive dashboards.
• Data cleaning or ETL — handle data preparation before invoking this skill.

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1. Tool Selection

Priority order based on capability and output quality:

Priority	Tool	Best For
1	matplotlib / seaborn	Full chart library — bar, line, pie, scatter, waterfall, histogram, heatmap, box plot, small multiples
2	mermaid	Gantt charts, pie charts (limited palette/styling)
3	ASCII table fallback	Text-only tables when no graphical rendering is available

Tool Details

• matplotlib: bar (vertical/horizontal), line, pie, scatter, waterfall, histogram, heatmap, stacked bar, grouped bar, small multiples, dashboard layouts.
• seaborn: statistical visualizations, distribution plots, violin plots, pair plots, regression plots. Use seaborn when the data story is statistical in nature.
• mermaid: Gantt charts and simple pie charts only. Limited styling control — use only when the presentation format natively supports mermaid rendering or when a quick timeline is needed.
• ASCII: text-based tables rendered with simple alignment. Last resort when Python is unavailable.

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2. Chart Type Selection Guide

Data Story	Chart Type	Notes
Compare categories	Bar (vertical or horizontal)	Horizontal if labels are long
Trend over time	Line	Add markers for key inflection points
Part-to-whole	Stacked bar or pie	Pie: max 5 segments, otherwise use stacked bar
Change decomposition	Waterfall	Show starting value, deltas, ending value
Correlation	Scatter	Add trend line if relationship is meaningful
Distribution	Histogram / box plot	Box plot for comparing distributions across groups
Ranking	Horizontal bar	Sort descending, accent color on top item
Timeline / schedule	Gantt (mermaid)	Use mermaid syntax for native rendering
Multiple KPIs	Dashboard / small multiples	Grid layout with consistent axes

When in doubt, default to a horizontal bar chart — it is the most readable chart type for presentations.

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3. Data Ingestion

From files (user provides path)

import pandas as pd
df = pd.read_csv(path)      # CSV
df = pd.read_json(path)     # JSON
df = pd.read_excel(path)    # Excel (requires openpyxl)

From inline data (user provides in conversation)

data = {
    "Category": ["A", "B", "C"],
    "Value": [100, 250, 175]
}
df = pd.DataFrame(data)

From project artifacts

• Check PROJECT.md, metrics/, or any data files referenced in the project.
• Parse structured sections (tables, KPI lists) into DataFrames.

From [PLACEHOLDER] tags

When no real data is available, generate realistic sample data with clear markers:

# [SAMPLE DATA] — replace with actual figures before final presentation
data = {
    "Region": ["APAC", "EMEA", "Americas"],
    "Revenue_M": [45, 62, 88]  # [SAMPLE DATA]
}

Always add [SAMPLE DATA] comments in code and [SAMPLE DATA] watermark text on the chart itself.

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4. Chart Generation Patterns

Standard matplotlib Pattern

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import os

# --- Configuration ---
ACCENT_COLOR = "#1a73e8"
NEUTRAL_COLOR = "#bdbdbd"
FONT_FAMILY = "sans-serif"
OUTPUT_DIR = ".presentations/output/assets"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# --- Create chart ---
fig, ax = plt.subplots(figsize=(10, 6))

categories = df["Category"]
values = df["Value"]
key_index = values.idxmax()  # Accent the key data point

colors = [ACCENT_COLOR if i == key_index else NEUTRAL_COLOR for i in range(len(values))]
bars = ax.barh(categories, values, color=colors)

# --- Insight title (not description) ---
ax.set_title("APAC grew 3x faster than other regions", fontsize=16, fontweight="bold", pad=20)

# --- Axis labels with units ---
ax.set_xlabel("Revenue ($M)", fontsize=12)

# --- Source line ---
fig.text(0.1, 0.02, "Source: Internal Finance Report, Q4 2025", fontsize=8, color="#757575")

# --- Clean styling ---
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.tick_params(axis="both", labelsize=11)

plt.tight_layout(rect=[0, 0.05, 1, 1])

# --- Save ---
chart_path = os.path.join(OUTPUT_DIR, "revenue_by_region.png")
fig.savefig(chart_path, dpi=150, bbox_inches="tight", facecolor="white")
plt.close(fig)

Waterfall Chart Pattern

import numpy as np

labels = ["Start", "Product A", "Product B", "Cost Reduction", "FX Impact", "End"]
values = [100, 30, 15, 10, -8, 147]
cumulative = np.cumsum(values)
cumulative = np.insert(cumulative, 0, 0)[:-1]

fig, ax = plt.subplots(figsize=(10, 6))

for i, (label, val) in enumerate(zip(labels, values)):
    color = ACCENT_COLOR if i in (0, len(labels) - 1) else ("#4caf50" if val > 0 else "#f44336")
    bottom = cumulative[i] if i not in (0, len(labels) - 1) else 0
    height = val if i not in (0, len(labels) - 1) else cumulative[i] + val
    ax.bar(label, height if i in (0, len(labels) - 1) else val, bottom=bottom, color=color)

ax.set_title("Revenue bridge: +47% driven by Product A", fontsize=16, fontweight="bold")
fig.text(0.1, 0.02, "Source: Finance, FY2025", fontsize=8, color="#757575")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
plt.tight_layout(rect=[0, 0.05, 1, 1])
chart_path = os.path.join(OUTPUT_DIR, "revenue_bridge.png")
fig.savefig(chart_path, dpi=150, bbox_inches="tight", facecolor="white")
plt.close(fig)

Consulting-Standard Annotations

• Insight title: The chart title states the takeaway, not a description. "APAC grew 3x faster" not "Revenue by Region".
• Callout boxes: Use ax.annotate() to highlight key data points with a box and arrow.
• Source line: Always present at bottom-left in small gray text.
• Accent color: One color for the key data point; neutral gray for everything else. Draw the viewer's eye to the insight.

# Callout annotation example
ax.annotate(
    "+47%",
    xy=(key_x, key_y),
    xytext=(key_x + 0.5, key_y + 10),
    fontsize=14, fontweight="bold", color=ACCENT_COLOR,
    arrowprops=dict(arrowstyle="->", color=ACCENT_COLOR, lw=1.5),
    bbox=dict(boxstyle="round,pad=0.3", facecolor="white", edgecolor=ACCENT_COLOR)
)

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5. Output Format

File output

• Save all chart images to .presentations/output/assets/.
• Preferred format: PNG at 150 DPI for slides, SVG when vector output is requested.
• File naming: {slide_number}_{chart_slug}.png (e.g., 04_revenue_by_region.png).

Return to presentation-renderer

Provide chart metadata for each generated asset:

chart_metadata = {
    "file_path": ".presentations/output/assets/04_revenue_by_region.png",
    "title": "APAC grew 3x faster than other regions",
    "source": "Internal Finance Report, Q4 2025",
    "key_insight": "APAC revenue growth outpaced EMEA and Americas by 3:1",
    "chart_type": "horizontal_bar",
    "slide_number": 4
}

Asset inventory

After generating all charts, produce a summary list:

Charts generated:
  [04] revenue_by_region.png — "APAC grew 3x faster than other regions"
  [07] revenue_bridge.png — "Revenue bridge: +47% driven by Product A"
  [09] customer_distribution.png — "Enterprise segment drives 68% of revenue"

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6. HARD RULES

These rules are non-negotiable and must be followed for every chart generated:

1. Chart title = insight, not description. Write what the data means, not what the chart shows. "APAC grew 3x faster" not "Revenue by Region".

2. Source line on every chart. Include data source and date at bottom-left in small gray text. No exceptions.

3. Accent color for key data point, neutral gray for everything else. One data point tells the story — make it visually dominant. Everything else recedes.

4. Never fabricate data. If real data is unavailable, use clearly marked sample data with [SAMPLE DATA] in the chart subtitle and code comments. The viewer must never mistake sample data for real data.

5. Axis labels always present with units. Every axis must have a label that includes the unit of measurement (e.g., "Revenue ($M)", "Growth (%)", "Users (thousands)").

6. Pie charts: max 5 segments. If more than 5 categories, group the smallest into "Other" or switch to a horizontal bar chart.

7. Save to the standard output directory. All assets go to .presentations/output/assets/ — never scatter chart files across the project.

8. Return metadata for every chart. The renderer needs file path, title, source, and key insight to embed charts correctly.

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Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Using pie charts for more than 5 categories	Humans cannot accurately compare angles; slices become unreadable past 5 segments	Use horizontal bar chart for comparisons; pie charts only for 2-3 clearly distinct proportions
3D effects on charts	Distorts proportions; back segments appear smaller; adds visual noise without information	Always use flat 2D charts; 3D effects deceive more than they inform
Not labeling axes or providing units	Audience guesses at scale; "Revenue" means nothing without currency and time period	Always include axis labels, units, time period, and data source; a chart should be self-explanatory
Using rainbow color palettes	Too many colors create visual chaos; colorblind users cannot distinguish many combinations	Use 2-3 brand colors with intensity variation; ensure sufficient contrast; test with colorblind simulator
Showing raw data tables when a chart would communicate better	Executives scan for trends and outliers, not individual numbers; tables require too much cognitive effort	Use charts for patterns and trends; tables only for exact reference values or small datasets (under 10 rows)