visualize

name	visualize
description	Generate charts and plots from data — line, bar, pie, heatmap, scatter, histogram
argument-hint	[dataset-name] [chart-type or description]
risk	safe
disable-model-invocation	false
user-invocable	true
allowed-tools	Read, Write, Bash, Glob, Grep
model	claude-haiku-4-5-20251001
context	fork
agent	general-purpose

10x Analyst — Visualizer

Generate publication-ready charts from any data file.

Overview

Creates matplotlib/seaborn visualizations with the 10x.in style. Reads from input/<dataset>/, saves charts to output/<dataset>/charts/. Specify a chart type or describe what you want to see.

When to Use

• User asks for a chart: "plot revenue over time", "show me a bar chart of top products"
• User wants to visualize a specific column or relationship
• User needs charts for a presentation or report

Path Resolution

Parse $ARGUMENTS:

• First word: dataset name → reads from input/<dataset-name>/
• Remaining text: chart type or description
• Charts saved to: output/<dataset-name>/charts/

Instructions

1. Parse $ARGUMENTS:
   • First word = dataset name
   • Rest = chart type or description (e.g., "line chart of revenue by month")
   • Input: input/<dataset>/
   • Output: output/<dataset>/charts/
2. Load the data file(s) from input/<dataset>/
3. Clean column names: lowercase, underscore-separated
4. Determine chart type from user's request:

User Says	Chart Type
"trend", "over time", "line"	Line chart
"top", "ranking", "bar"	Bar chart (horizontal for >5 categories)
"breakdown", "proportion", "pie"	Donut chart
"correlation", "heatmap", "relationship"	Heatmap
"distribution", "histogram"	Histogram
"scatter", "compare two"	Scatter plot
"box", "spread"	Box plot

5. Create output directory: mkdir -p output/<dataset>/charts
6. Write and execute a Python script:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# 10x style
COLORS = ['#FF6B35', '#004E89', '#00A878', '#FFD166', '#EF476F', '#118AB2', '#073B4C']
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette(COLORS)
plt.rcParams.update({'figure.figsize': (12, 6), 'figure.dpi': 150, 'font.size': 11})

# Load data from input/<dataset>/
df = pd.read_csv('input/<dataset>/file.csv')

# Generate chart
fig, ax = plt.subplots()
# ... chart-specific code ...
ax.set_title('Chart Title — Key Takeaway', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('output/<dataset>/charts/chart_name.png', bbox_inches='tight')
plt.close()
print("Chart saved to output/<dataset>/charts/chart_name.png")

7. Show the user the file path to the saved chart

Examples

# Specific chart type
/10x-analyst:visualize shopify-data "line chart of revenue by month"

# Auto-detect best chart
/10x-analyst:visualize shopify-data "show me the top 10 products"

# Multiple charts
/10x-analyst:visualize shopify-data "revenue trend and customer segments"

Limitations

• Static PNG output only — for interactive charts use :dashboard
• Max 15 categories per chart (remainder grouped as "Other")
• Requires matplotlib and seaborn installed

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Developed by 10x.in | 10x-Analyst v1.0.0