Chart Visualization Agent Skill

by HezaoHezao

Generate charts: select type, extract data, render image.

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

117

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/HezaoHezao/poirot --skill chart-visualization

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

9 sections, as written by the author

Chart Visualization

Overview

Transform data into visual charts. Intelligently select the most suitable chart

type, extract parameters, and generate a chart image.

> Poirot note: The original deer-flow skill uses a bundled

> scripts/generate.js (Node.js + charting library). Poirot doesn't bundle

> that script. Use bash with Python (matplotlib/plotly) as the rendering

> engine instead. Install: pip install matplotlib plotly.

Chart Selection Guide

| Data Pattern | Recommended Chart | When |

|---|---|---|

| Time Series | Line / Area | Trends over time |

| Comparisons | Bar / Column | Categorical comparison |

| Distribution | Histogram / Boxplot | Frequency distribution |

| Part-to-Whole | Pie / Treemap | Proportions |

| Relationships | Scatter | Correlation |

| Flow | Sankey | Flow between stages |

| Multi-dimensional | Radar | Compare across dimensions |

| Process | Funnel | Stage conversion |

| Hierarchy | Org chart / Mind map | Tree structure |

| Geographic | Map | Spatial data |

Workflow

1. Select Chart Type

Analyze the user's data features:

Time dimension? → Line/Area
Categories? → Bar/Column
Proportions? → Pie/Treemap
Correlation? → Scatter
Flow? → Sankey
Multiple dimensions? → Radar

2. Prepare Data

Extract data from user input, format as Python data structure:

data = {
    "labels": ["Jan", "Feb", "Mar", "Apr", "May"],
    "values": [120, 150, 180, 200, 220],
    "title": "Monthly Revenue",
    "xlabel": "Month",
    "ylabel": "Revenue ($K)"
}

3. Generate Chart

python3 -c "
import matplotlib
matplotlib.use('Agg')  # non-interactive backend
import matplotlib.pyplot as plt

labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
values = [120, 150, 180, 200, 220]

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(labels, values, marker='o', linewidth=2, markersize=8)
ax.set_title('Monthly Revenue', fontsize=16, fontweight='bold')
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Revenue ($K)', fontsize=12)
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('.poirot/outputs/chart.png', dpi=150, bbox_inches='tight')
print('Saved to .poirot/outputs/chart.png')
"

Common Chart Types via matplotlib

# Bar chart
python3 -c "
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
cats = ['A', 'B', 'C', 'D']
vals = [23, 45, 12, 67]
plt.bar(cats, vals, color=['#4CAF50', '#2196F3', '#FF9800', '#F44336'])
plt.title('Category Comparison')
plt.savefig('.poirot/outputs/bar.png', dpi=150)
"

# Scatter plot
python3 -c "
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(100)
y = x * 0.8 + np.random.randn(100) * 0.5
plt.scatter(x, y, alpha=0.6, c='steelblue')
plt.title('Correlation Scatter')
plt.savefig('.poirot/outputs/scatter.png', dpi=150)
"

# Pie chart
python3 -c "
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
labels = ['Product A', 'Product B', 'Product C']
sizes = [45, 35, 20]
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
plt.title('Market Share')
plt.savefig('.poirot/outputs/pie.png', dpi=150)
"

Pitfalls

matplotlib backend: always use matplotlib.use('Agg') for non-interactive

(headless) rendering. Without it, matplotlib may try to open a GUI window.

Chinese characters: matplotlib may not render CJK by default. Set font:

plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS']

DPI: use dpi=150 for crisp images. dpi=300 for print quality.
File size: PNG is standard. Use SVG for vector (plt.savefig('chart.svg')).
Color palettes: use colorblind-friendly palettes. Avoid red/green only.

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How to use it

Copy the folder

Take hezaohezao/chart-visualization from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.