Data Viz Deck

Data Visualization & Deck Builder

Transform structured data and analysis into polished visual deliverables: presentation decks, interactive dashboards, and visual reports.

Install

git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/data-viz-deck ~/.claude/skills/

When to Use This Skill

• User has completed an audit or analysis and wants a visual deliverable
• User says "make a deck," "create a presentation," "build a dashboard," "visualize this"
• User wants to turn a markdown report into client-ready slides
• User needs charts, tables, or visual summaries from performance data

Output Formats

1. PPTX Deck (Primary)

Native PowerPoint with editable charts, styled tables, and professional layouts. Best for client handoffs and presentations.

Requires: python-pptx (installed), pandas (installed)

2. Interactive HTML Dashboard

Single-file HTML with plotly.js charts (loaded via CDN), filterable tables, and responsive layout. Best for sharing interactive reports.

Requires: jinja2 (installed), pandas (installed). Plotly.js loaded via CDN at runtime.

3. Visual Markdown Report

Enhanced markdown with embedded chart images (requires matplotlib: pip install matplotlib). Best for vault reports and documentation.

Workflow

Step 1: Identify the Data Source

Read the source file (audit markdown, CSV, JSON, or database query results). Parse the key metrics, tables, and findings into a pandas DataFrame or structured dict.

Step 2: Select Chart Types

Step 3: Apply the Design System

All deliverables use a configurable design system. Override the color tokens and font choices to match your brand.

Color Palette

COLORS = {
    # Brand accent -- change to your brand color
    "accent":      "#3D7A5C",  # Forest green
    "accent_light":"#6AB88A",  # Light green

    # Chart series (ordered for visual distinction)
    "series": ["#3D7A5C", "#6AB88A", "#8B7EC8", "#D4845A", "#C9A84C", "#C75B6F", "#93C9A8", "#8B949E"],

    # Semantic
    "good":        "#2E8B57",  # Green -- above benchmark
    "warning":     "#D97706",  # Amber -- watch
    "critical":    "#C0392B",  # Red -- action needed
    "neutral":     "#8B949E",  # Slate -- no judgment

    # Dark mode (title slides, section dividers)
    "bg_deep":     "#141414",
    "bg_surface":  "#1E1E1E",
    "text_bright": "#F0F3F6",
    "text_secondary_dark": "#9CA3AF",

    # Light mode (content slides -- the default)
    "bg_light":    "#FAF7F2",  # Warm cream, NOT white
    "bg_surface_light": "#F2EDE6",
    "text_primary":"#0F0E0E",
    "text_secondary": "#57606A",
    "text_muted":  "#8B949E",
}

Color rules:

• Light mode (#FAF7F2 warm cream) is default for all content slides. NOT white.
• Dark mode (#141414) only for: title slides, section dividers, closing slides.
• Accent color used sparingly: top-performing bars, key metrics, accent callouts.
• Semantic colors (green/amber/red) only for status indicators, never decoration.

Typography (PPTX)

FONTS = {
    "title":    "Switzer",        # Slide titles -- Light (300), 28-36pt, NOT bold
    "subtitle": "Switzer",        # Subtitles -- Regular (400), 18-22pt
    "body":     "Switzer",        # Body text -- Regular (400), 12-14pt
    "data":     "Cartograph CF",  # Numbers, tables -- Regular/Bold, 11-12pt
    "kpi":      "Cartograph CF",  # KPI big numbers -- Bold (700), 44-60pt
    "label":    "Cartograph CF",  # ALL CAPS labels -- Regular, 9-10pt, +tracking
}
# Fallbacks: Switzer -> Inter -> system-ui | Cartograph CF -> Consolas -> monospace

Critical typography rules: 1. Headlines use Light weight, NOT Bold. Light at large sizes = confident, premium. 2. Monospace font for ALL numeric/data content. KPIs, table numbers, labels, footers. 3. Negative tracking on headlines. -0.03em to -0.04em. 4. Positive tracking on ALL CAPS labels. +0.08em to +0.1em.

Slide Layouts

Every deck follows this structure:

1. Title Slide         -- Report name, date (dark mode)
2. Executive Summary   -- 3-5 KPI tiles + key findings bullets
3. Scorecard           -- Color-coded performance overview table
4. Deep Dive slides    -- One per major finding (chart + insight + recommendation)
5. Opportunity Sizing  -- Waterfall or bar chart of revenue opportunities
6. Recommendations     -- Prioritized table (Quick Wins / Strategic / Maintenance)
7. Appendix            -- Full data tables

Slide Dimensions (python-pptx)

Width:  13.333"
Height: 7.5"
Aspect: 16:9

Step 4: Generate the Deliverable

Follow the code patterns in REFERENCE.md for the chosen output format.

Key Principles

1. One insight per slide. Never cram multiple findings onto one slide. Each deep-dive slide has: chart (left 60%), insight + recommendation (right 40%).

2. Lead with the number. Every slide title should contain the key metric: "Post-Purchase RPR at $0.07 -- 14x Below Benchmark" not "Post-Purchase Flow Analysis."

3. Benchmark everything. Never show a metric without context. Show the benchmark, the gap, and what closing the gap is worth.

4. Color = meaning. Green means good/above benchmark. Amber means watch. Red means action needed. Never use color decoratively in data slides.

5. Simplify chart data. Max 6-8 items per chart. Aggregate the tail into "Other."

6. Executive summary is the deck. If someone only reads slide 2, they should understand the full story.

7. Never estimate when actuals exist. If the data has exact values, compute precise totals.

8. No white backgrounds. Always warm cream #FAF7F2 for light content.

9. No bold headlines. Light weight for all hero/section titles.

For complete code templates and reference, see REFERENCE.md.

Data Viz Deck — Examples

Example 1: Email Marketing Audit Deck (PPTX)

Prompt

Create a presentation deck from this Klaviyo audit data. The account has 8 flows (3 missing essential ones), $140K flow revenue, $918K campaign revenue. Open rate is 22% (benchmark 25%), click rate 1.8% (benchmark 3%). Include title slide, KPI scorecard, flow performance chart, and recommendations.

What the skill does

1. Parses the audit metrics into structured data 2. Creates a PPTX with python-pptx:

• Slide 1 (dark): Title slide with report name, date, brand lockup
• Slide 2 (light): Executive Summary with 4 KPI tiles ($1.06M total revenue, $140K flow revenue, 22% open rate in amber, 1.8% click rate in red)
• Slide 3 (light): Horizontal bar chart of flow revenue (sorted descending), insight panel noting 3 missing flows
• Slide 4 (light): Benchmark comparison table (flows x metrics, color-coded cells)
• Slide 5 (light): Recommendations table (Quick Wins / Strategic / Maintenance)
• Slide 6 (dark): Closing slide

Key code pattern

from pptx import Presentation
from pptx.util import Inches, Pt

prs = Presentation()
prs.slide_width = Inches(13.333)
prs.slide_height = Inches(7.5)

# KPI tiles
kpis = [
    {"label": "Total Revenue", "value": "$1,058,829", "color": ACCENT},
    {"label": "Flow Revenue", "value": "$140,149", "subtitle": "13.2% of total"},
    {"label": "Open Rate", "value": "22.4%", "color": AMBER, "subtitle": "Benchmark: 25%"},
    {"label": "Click Rate", "value": "1.8%", "color": RED, "subtitle": "Benchmark: 3%"},
]
make_kpi_slide(prs, "Executive Summary", kpis)
prs.save("email-audit-deck.pptx")

---

Example 2: Interactive Campaign Dashboard (HTML)

Prompt

Build an HTML dashboard showing campaign performance across 4 channels (Email, SMS, Social, Paid) with KPI tiles for total revenue, average ROI, and conversion rate. Include a bar chart comparing channel revenue and a funnel chart showing the email conversion path.

What the skill does

1. Structures the data as JSON for plotly.js 2. Generates a single-file HTML dashboard with:

• Dark header with title and date
• KPI row (3 tiles: $285K revenue in green, 4.2x ROI, 2.8% conversion)
• 2-column grid layout:
• Left: Channel revenue bar chart (plotly.js)
• Right: Email funnel chart (Sent > Delivered > Opened > Clicked > Converted)
• Insight boxes with left accent borders
• Responsive layout (collapses to single column on mobile)

3. All CSS is inline, plotly.js loaded via CDN

Key code pattern

chart_configs = {
    "channel-revenue": bar_chart_config(
        categories=["Email", "SMS", "Social", "Paid"],
        values=[142000, 58000, 45000, 40000],
        title="Revenue by Channel"
    ),
    "email-funnel": funnel_chart_config(
        stages=["Sent", "Delivered", "Opened", "Clicked", "Converted"],
        values=[50000, 48500, 12125, 2182, 654],
        title="Email Conversion Funnel"
    )
}

---

Example 3: Monthly Performance Report with Charts (Markdown + Images)

Prompt

Generate a visual markdown report with embedded charts showing month-over-month trends for revenue, subscriber growth, and engagement rates over the last 6 months. Include benchmark comparison bars.

What the skill does

1. Creates matplotlib charts saved as PNG:

• Line chart: Revenue trend (6 months) with benchmark reference line
• Grouped bar chart: Subscriber growth (new vs churned) per month
• Benchmark comparison: Current metrics vs industry averages

2. Generates a markdown report with ![]() image embeds:

# Monthly Performance Report — March 2026

## Revenue Trend
![Revenue Trend](./charts/revenue-trend.png)

**Key Insight:** Revenue grew 18% MoM, crossing the $50K benchmark in February.

## Subscriber Health
![Subscriber Growth](./charts/subscriber-growth.png)

**Key Insight:** Net subscriber growth turned positive in January after 3 months of decline.

Key code pattern

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

months = ["Oct", "Nov", "Dec", "Jan", "Feb", "Mar"]
revenue = [38200, 41500, 39800, 44100, 51200, 52400]

save_horizontal_bar(
    categories=months,
    values=revenue,
    title="Monthly Email Revenue",
    filepath="./charts/revenue-trend.png",
    value_format="${:,.0f}"
)

Data Viz Deck — Technical Reference

PPTX Generation with python-pptx

Slide Dimensions & Setup

from pptx import Presentation
from pptx.util import Inches, Pt, Emu
from pptx.dml.color import RGBColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
from pptx.enum.chart import XL_CHART_TYPE, XL_LEGEND_POSITION, XL_LABEL_POSITION
from pptx.enum.shapes import MSO_SHAPE
from pptx.chart.data import CategoryChartData, ChartData

prs = Presentation()
prs.slide_width = Inches(13.333)  # 16:9 widescreen
prs.slide_height = Inches(7.5)
SLIDE_LAYOUT = prs.slide_layouts[6]  # Blank layout

Color Constants

from pptx.dml.color import RGBColor

# Brand accent -- customize to match your brand
ACCENT       = RGBColor(0x3D, 0x7A, 0x5C)
ACCENT_LIGHT = RGBColor(0x6A, 0xB8, 0x8A)

# Semantic
GREEN        = RGBColor(0x2E, 0x8B, 0x57)
AMBER        = RGBColor(0xD9, 0x77, 0x06)
RED          = RGBColor(0xC0, 0x39, 0x2B)
SLATE        = RGBColor(0x8B, 0x94, 0x9E)

# Chart series colors (use in order)
SERIES_COLORS = [
    ACCENT, ACCENT_LIGHT,
    RGBColor(0x8B, 0x7E, 0xC8),  # Lavender
    RGBColor(0xD4, 0x84, 0x5A),  # Terracotta
    RGBColor(0xC9, 0xA8, 0x4C),  # Gold
    RGBColor(0xC7, 0x5B, 0x6F),  # Dusty Rose
    RGBColor(0x93, 0xC9, 0xA8),  # Mist
    SLATE,
]

# Dark mode (title slides, section dividers)
BG_DEEP      = RGBColor(0x14, 0x14, 0x14)
BG_SURFACE   = RGBColor(0x1E, 0x1E, 0x1E)
TEXT_BRIGHT  = RGBColor(0xF0, 0xF3, 0xF6)
TEXT_SEC_DK  = RGBColor(0x9C, 0xA3, 0xAF)
TEXT_MUTED_DK= RGBColor(0x6E, 0x76, 0x81)

# Light mode (content slides)
BG_LIGHT     = RGBColor(0xFA, 0xF7, 0xF2)  # Warm cream
BG_CARD      = RGBColor(0xF2, 0xED, 0xE6)
TEXT_PRIMARY  = RGBColor(0x0F, 0x0E, 0x0E)
TEXT_SECONDARY= RGBColor(0x57, 0x60, 0x6A)
TEXT_MUTED   = RGBColor(0x8B, 0x94, 0x9E)

Helper Functions

def add_textbox(slide, left, top, width, height, text, font_size=12,
                bold=False, color=TEXT_PRIMARY, alignment=PP_ALIGN.LEFT,
                font_name="Switzer"):
    txBox = slide.shapes.add_textbox(Inches(left), Inches(top), Inches(width), Inches(height))
    tf = txBox.text_frame
    tf.word_wrap = True
    p = tf.paragraphs[0]
    p.text = text
    p.font.size = Pt(font_size)
    p.font.bold = bold
    p.font.color.rgb = color
    p.font.name = font_name
    p.alignment = alignment
    return txBox


def add_shape_bg(slide, left, top, width, height, fill_color=BG_CARD, corner_radius=None):
    shape = slide.shapes.add_shape(
        MSO_SHAPE.ROUNDED_RECTANGLE, Inches(left), Inches(top), Inches(width), Inches(height)
    )
    shape.fill.solid()
    shape.fill.fore_color.rgb = fill_color
    shape.line.fill.background()
    if corner_radius:
        shape.adjustments[0] = corner_radius
    return shape


def set_cell_style(cell, text, font_size=11, bold=False, color=TEXT_PRIMARY,
                   alignment=PP_ALIGN.LEFT, font_name="Switzer", fill_color=None):
    cell.text = str(text)
    for paragraph in cell.text_frame.paragraphs:
        paragraph.font.size = Pt(font_size)
        paragraph.font.bold = bold
        paragraph.font.color.rgb = color
        paragraph.font.name = font_name
        paragraph.alignment = alignment
    cell.vertical_anchor = MSO_ANCHOR.MIDDLE
    if fill_color:
        cell.fill.solid()
        cell.fill.fore_color.rgb = fill_color


def benchmark_color(value, good_threshold, warning_threshold, higher_is_better=True):
    if higher_is_better:
        return GREEN if value >= good_threshold else (AMBER if value >= warning_threshold else RED)
    else:
        return GREEN if value <= good_threshold else (AMBER if value <= warning_threshold else RED)

Slide Templates

Title Slide (Dark Mode)

def make_title_slide(prs, title, subtitle, date_str):
    slide = prs.slides.add_slide(SLIDE_LAYOUT)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, prs.slide_width, prs.slide_height)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG_DEEP
    bg.line.fill.background()
    add_textbox(slide, 0.8, 1.5, 11, 1.5, title, font_size=54, bold=False, color=TEXT_BRIGHT)
    add_textbox(slide, 0.8, 3.2, 11, 0.8, subtitle, font_size=20, color=TEXT_SEC_DK)
    add_textbox(slide, 0.8, 5.5, 5, 0.5, date_str, font_size=14, color=TEXT_MUTED_DK, font_name="Cartograph CF")
    return slide

KPI Scorecard Slide

def make_kpi_slide(prs, title, kpis):
    """kpis: list of dicts with keys: label, value, subtitle, color (optional)"""
    slide = prs.slides.add_slide(SLIDE_LAYOUT)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, prs.slide_width, prs.slide_height)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG_LIGHT
    bg.line.fill.background()
    add_textbox(slide, 0.8, 0.4, 11, 0.6, title, font_size=32, bold=False, color=TEXT_PRIMARY)
    n = len(kpis)
    tile_width = min(2.4, (11.5 / n) - 0.3)
    for i, kpi in enumerate(kpis):
        x = 0.8 + i * (tile_width + 0.3)
        add_shape_bg(slide, x, 1.4, tile_width, 2.2, fill_color=BG_CARD)
        add_textbox(slide, x + 0.2, 1.6, tile_width - 0.4, 1.0, kpi["value"],
                    font_size=44, bold=True, color=kpi.get("color", ACCENT),
                    alignment=PP_ALIGN.CENTER, font_name="Cartograph CF")
        add_textbox(slide, x + 0.2, 2.6, tile_width - 0.4, 0.4, kpi["label"],
                    font_size=12, alignment=PP_ALIGN.CENTER)
        if kpi.get("subtitle"):
            add_textbox(slide, x + 0.2, 3.0, tile_width - 0.4, 0.4, kpi["subtitle"],
                        font_size=10, color=TEXT_MUTED, alignment=PP_ALIGN.CENTER,
                        font_name="Cartograph CF")
    return slide

Bar Chart Slide

def make_bar_chart_slide(prs, title, categories, values, value_label="Revenue",
                         insight_text="", recommendation="", chart_color=ACCENT):
    """Bar chart on left 60%, insight text on right 40%."""
    slide = prs.slides.add_slide(SLIDE_LAYOUT)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, prs.slide_width, prs.slide_height)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG_LIGHT
    bg.line.fill.background()
    add_textbox(slide, 0.8, 0.4, 11, 0.6, title, font_size=24, bold=False, color=TEXT_PRIMARY)
    chart_data = CategoryChartData()
    chart_data.categories = categories
    chart_data.add_series(value_label, values)
    chart_frame = slide.shapes.add_chart(
        XL_CHART_TYPE.BAR_CLUSTERED, Inches(0.8), Inches(1.3), Inches(7.2), Inches(5.5), chart_data
    )
    chart = chart_frame.chart
    chart.has_legend = False
    plot = chart.plots[0]
    plot.gap_width = 80
    series = plot.series[0]
    series.format.fill.solid()
    series.format.fill.fore_color.rgb = chart_color
    series.has_data_labels = True
    data_labels = series.data_labels
    data_labels.font.size = Pt(10)
    data_labels.font.color.rgb = TEXT_PRIMARY
    data_labels.font.name = "Cartograph CF"
    data_labels.number_format = "$#,##0"
    if insight_text:
        add_shape_bg(slide, 8.3, 1.3, 4.5, 3.0, fill_color=BG_CARD)
        add_textbox(slide, 8.5, 1.5, 4.1, 0.4, "KEY INSIGHT", font_size=11, color=ACCENT, font_name="Cartograph CF")
        add_textbox(slide, 8.5, 2.0, 4.1, 2.0, insight_text, font_size=12, color=TEXT_PRIMARY)
    return slide

---

Interactive HTML Dashboard (Jinja2 + Plotly.js)

Plotly Config Patterns

PLOTLY_ACCENT = "#3D7A5C"
PLOTLY_SERIES = ["#3D7A5C", "#6AB88A", "#8B7EC8", "#D4845A", "#C9A84C", "#C75B6F", "#93C9A8", "#8B949E"]
PLOTLY_FONT = "Switzer, Inter, system-ui, sans-serif"
PLOTLY_MONO = "Cartograph CF, Consolas, monospace"

def bar_chart_config(categories, values, title="", color=PLOTLY_ACCENT, orientation="h"):
    return {
        "data": [{
            "type": "bar",
            "x": values if orientation == "h" else categories,
            "y": categories if orientation == "h" else values,
            "orientation": orientation,
            "marker": {"color": color},
            "text": [f"${v:,.0f}" for v in values] if any(v > 100 for v in values) else values,
            "textposition": "outside",
            "textfont": {"family": PLOTLY_MONO},
        }],
        "layout": {
            "title": {"text": title, "font": {"size": 16, "family": PLOTLY_FONT}},
            "margin": {"l": 200, "t": 50, "r": 50, "b": 50},
            "plot_bgcolor": "rgba(0,0,0,0)",
            "paper_bgcolor": "rgba(0,0,0,0)",
        }
    }

def doughnut_chart_config(labels, values, title=""):
    return {
        "data": [{
            "type": "pie", "labels": labels, "values": values, "hole": 0.5,
            "marker": {"colors": PLOTLY_SERIES[:len(labels)]},
            "textinfo": "label+percent", "textposition": "outside",
        }],
        "layout": {
            "title": {"text": title, "font": {"size": 16, "family": PLOTLY_FONT}},
            "showlegend": True, "legend": {"orientation": "h", "y": -0.15},
            "paper_bgcolor": "rgba(0,0,0,0)",
        }
    }

def funnel_chart_config(stages, values, title=""):
    return {
        "data": [{
            "type": "funnel", "y": stages, "x": values,
            "textinfo": "value+percent initial",
            "marker": {"color": PLOTLY_SERIES[:len(stages)]},
        }],
        "layout": {
            "title": {"text": title, "font": {"size": 16, "family": PLOTLY_FONT}},
            "margin": {"l": 150, "t": 50},
            "paper_bgcolor": "rgba(0,0,0,0)", "plot_bgcolor": "rgba(0,0,0,0)",
        }
    }

---

Matplotlib Charts (for Markdown/Image Output)

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    "figure.facecolor": "#FAF7F2",
    "axes.facecolor": "#F2EDE6",
    "axes.edgecolor": "#D6D0C8",
    "text.color": "#0F0E0E",
    "font.size": 11,
})

def save_horizontal_bar(categories, values, title, filepath, color="#3D7A5C",
                        value_format="${:,.0f}", figsize=(10, 6)):
    fig, ax = plt.subplots(figsize=figsize)
    y_pos = np.arange(len(categories))
    bars = ax.barh(y_pos, values, color=color, height=0.6)
    ax.set_yticks(y_pos)
    ax.set_yticklabels(categories)
    ax.invert_yaxis()
    ax.set_title(title, fontsize=16, fontweight="light", pad=15)
    for bar, val in zip(bars, values):
        ax.text(bar.get_width() + max(values) * 0.02, bar.get_y() + bar.get_height() / 2,
                value_format.format(val), va="center", fontsize=10, fontfamily="monospace")
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.xaxis.set_visible(False)
    plt.tight_layout()
    fig.savefig(filepath, dpi=150, bbox_inches="tight")
    plt.close(fig)

---

Data Parsing Utilities

import pandas as pd
import re

def parse_md_table(md_text, table_index=0):
    tables = []
    lines = md_text.split("\\n")
    i = 0
    while i < len(lines):
        line = lines[i].strip()
        if line.startswith("|") and i + 1 < len(lines) and "---" in lines[i + 1]:
            headers = [h.strip() for h in line.split("|")[1:-1]]
            rows = []
            i += 2
            while i < len(lines) and lines[i].strip().startswith("|"):
                row = [c.strip() for c in lines[i].strip().split("|")[1:-1]]
                rows.append(row)
                i += 1
            tables.append(pd.DataFrame(rows, columns=headers))
        i += 1
    return tables[table_index] if table_index < len(tables) else tables

def parse_currency(val):
    if isinstance(val, (int, float)):
        return float(val)
    return float(re.sub(r"[,$]", "", str(val))) if val and val.strip() else 0.0

def parse_percentage(val):
    if isinstance(val, (int, float)):
        return float(val)
    return float(re.sub(r"%", "", str(val))) if val and val.strip() else 0.0

---

Dependencies

Already Installed

• python-pptx -- PPTX generation
• pandas -- Data processing
• numpy -- Numerical operations
• jinja2 -- HTML templating
• Pillow -- Image handling

Install If Needed

pip install matplotlib    # PNG chart generation
pip install plotly        # Local plotly (optional -- CDN works for HTML)

python-pptx Chart Types

• BAR_CLUSTERED, BAR_STACKED -- Horizontal bars
• COLUMN_CLUSTERED, COLUMN_STACKED -- Vertical bars
• LINE, LINE_MARKERS -- Line charts
• PIE, DOUGHNUT -- Composition charts
• AREA, AREA_STACKED -- Area charts
• XY_SCATTER -- Scatter plots