chart-generation

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Use this skill for generating data-driven charts and visualizations using Python. Triggers: "create chart", "generate graph", "plot data", "visualize data", "bar chart", "line chart", "pie chart", "comparison chart", "positioning matrix", "trend chart", "market size chart", "TAM SAM SOM", "growth chart", "data visualization" Outputs: PNG/SVG chart images with accurate data representation. Used by: competitive-intel-agent, market-researcher-agent, pitch-deck-agent, review-analyst-agent

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Sourcemichaelboeding/skills
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NPX Install

npx skill4agent add michaelboeding/skills chart-generation

Tags

Translated version includes tags in frontmatter
data-visualizationpython-chart-generationmatplotlib-plotlychart-scriptstam-sam-som

SKILL.md Content

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Chart Generation Skill

Generate accurate, data-driven charts and visualizations using Python (matplotlib/plotly).
Use this for real data. For concept art and illustrations, use 
image-generation
instead.

What It Produces

Chart TypeUse CaseScript
Bar ChartCompare values across categories
bar_chart.py
Line ChartShow trends over time
line_chart.py
Pie ChartShow proportions/percentages
pie_chart.py
Positioning Matrix2x2 competitive positioning
positioning_matrix.py
Comparison TableFeature comparison grid
comparison_table.py
TAM/SAM/SOMMarket size visualization
tam_sam_som.py

Prerequisites

bash
pip install matplotlib numpy pillow
No API keys required - runs locally.

When to Use This vs Image Generation

ScenarioUse ThisUse image-generation
Real data from analysis✅❌
Accurate numbers/labels✅❌
Reproducible charts✅❌
Concept/mockup visuals❌✅
Artistic illustrations❌✅
Icons and graphics❌✅

Chart Types

1. Bar Chart

Compare values across categories.
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/bar_chart.py \
  --labels '["Product A", "Product B", "Product C"]' \
  --values '[85, 62, 45]' \
  --title "Feature Comparison" \
  --ylabel "Score" \
  --output bar_chart.png
Options:
  • --horizontal
    - Horizontal bars instead of vertical
  • --colors
    - Custom colors: 
    '["#4CAF50", "#2196F3", "#FF9800"]'
  • --show-values
    - Display values on bars

2. Line Chart

Show trends over time or progression.
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/line_chart.py \
  --x '["Jan", "Feb", "Mar", "Apr", "May", "Jun"]' \
  --y '[100, 150, 180, 220, 310, 450]' \
  --title "Monthly Revenue Growth" \
  --xlabel "Month" \
  --ylabel "Revenue ($K)" \
  --output growth_chart.png
Options:
  • --multi
    - Multiple lines: 
    --y '[[100,150,200], [80,120,180]]' --legend '["Product A", "Product B"]'
  • --fill
    - Fill area under line
  • --markers
    - Show data point markers

3. Pie Chart

Show proportions and percentages.
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/pie_chart.py \
  --labels '["Engineering", "Marketing", "Sales", "Operations"]' \
  --values '[40, 25, 20, 15]' \
  --title "Use of Funds" \
  --output pie_chart.png
Options:
  • --donut
    - Donut chart (hollow center)
  • --explode
    - Explode a slice: 
    --explode 0
    (first slice)
  • --show-percent
    - Show percentages on slices

4. Positioning Matrix (2x2)

Competitive positioning on two axes.
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/positioning_matrix.py \
  --companies '["Your Product", "Competitor A", "Competitor B", "Competitor C"]' \
  --x-values '[70, 90, 50, 30]' \
  --y-values '[80, 85, 60, 45]' \
  --x-label "Price (Low → High)" \
  --y-label "Features (Basic → Advanced)" \
  --title "Competitive Positioning" \
  --output positioning.png
Options:
  • --quadrant-labels
    - Label quadrants: 
    '["Niche", "Leaders", "Laggards", "Challengers"]'
  • --highlight
    - Highlight your position: 
    --highlight 0
  • --sizes
    - Bubble sizes for market share

5. Comparison Table

Feature comparison grid as an image.
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/comparison_table.py \
  --features '["Feature A", "Feature B", "Feature C", "Feature D"]' \
  --companies '["You", "Comp A", "Comp B"]' \
  --data '[["✓", "✓", "✗"], ["✓", "✗", "✓"], ["✓", "✓", "✓"], ["✓", "✗", "✗"]]' \
  --title "Feature Comparison" \
  --output comparison.png
Options:
  • --highlight-column
    - Highlight your column: 
    --highlight-column 0
  • --colors
    - Use colors instead of symbols

6. TAM/SAM/SOM Chart

Market size visualization (concentric circles).
bash
python3 ${SKILL_PATH}/skills/chart-generation/scripts/tam_sam_som.py \
  --tam 50 \
  --sam 8 \
  --som 0.5 \
  --unit "B" \
  --title "Market Opportunity" \
  --output market_size.png
Options:
  • --unit
    - "B" for billions, "M" for millions
  • --labels
    - Custom labels: 
    '["Total Market", "Serviceable", "Obtainable"]'

Usage by Other Skills

competitive-intel-agent

python
# Generate positioning matrix from analysis
positioning_matrix.py \
  --companies '["You", "Salesforce", "HubSpot"]' \
  --x-values '[30, 95, 70]' \
  --y-values '[75, 90, 60]'

market-researcher-agent

python
# Generate TAM/SAM/SOM from research
tam_sam_som.py --tam 120 --sam 15 --som 2.5 --unit "B"

pitch-deck-agent

python
# Generate traction chart
line_chart.py \
  --x '["Q1", "Q2", "Q3", "Q4"]' \
  --y '[50, 120, 280, 500]' \
  --title "Revenue Growth"

review-analyst-agent

python
# Generate sentiment distribution
pie_chart.py \
  --labels '["Positive", "Neutral", "Negative"]' \
  --values '[65, 20, 15]' \
  --title "Review Sentiment"

Output Formats

All scripts support:
  • --output file.png
    - PNG image (default)
  • --output file.svg
    - SVG vector
  • --output file.pdf
    - PDF document

Styling Options

All scripts support these common options:
OptionDescriptionExample
--title
Chart title
"Monthly Revenue"
--width
Width in inches
12
--height
Height in inches
8
--dpi
Resolution
150
--style
Matplotlib style
"seaborn"
, 
"dark_background"
--colors
Custom color palette
'["#4CAF50", "#2196F3"]'
--font-size
Base font size
12

Integration Pattern

Higher-level skills call chart-generation like this:
markdown
## In competitive-intel-agent workflow:

1. Analyze competitors (gather data)
2. Structure data as JSON
3. Call chart-generation script with data
4. Embed resulting PNG in report
Example flow:
python
# 1. Analysis produces this data
data = {
    "companies": ["You", "Competitor A", "Competitor B"],
    "features": [8, 6, 5],
    "prices": [29, 49, 39]
}

# 2. Generate chart
python3 bar_chart.py \
  --labels '["You", "Competitor A", "Competitor B"]' \
  --values '[8, 6, 5]' \
  --title "Feature Count Comparison" \
  --output features.png

# 3. Embed in report
![Feature Comparison](features.png)

Example Prompts

Direct chart creation:
"Create a bar chart comparing our features to competitors"
As part of analysis:
"Analyze these companies and generate a positioning matrix"
Data visualization:
"Plot our monthly revenue growth from this data: [100, 150, 220, 350]"
Market sizing:
"Create a TAM/SAM/SOM chart: TAM $50B, SAM $5B, SOM $500M"