visualization — installing visualization libraries for Python visualization, aws-cur-report-generator, community, installing visualization libraries for Python, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Data Analysis Agents needing interactive chart generation capabilities with pyecharts and Apache ECharts. Visualization is a skill that generates interactive charts and graphs using technologies like pyecharts and Apache ECharts, enabling developers to create informative and engaging data visualizations.

Features

Generates self-contained HTML with embedded JS using Apache ECharts
Supports various chart types, including bar charts via pyecharts
Utilizes pyecharts options, such as title_opts and tooltip_opts, for customization
Creates interactive charts with add_xaxis and add_yaxis functions
Enables developers to set global options, including title and tooltip settings

# Core Topics

T-Klug T-Klug
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The visualization skill by T-Klug is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for installing visualization libraries for Python.

Ideal Agent Persona

Perfect for Data Analysis Agents needing interactive chart generation capabilities with pyecharts and Apache ECharts.

Core Value

Empowers agents to create self-contained HTML with embedded JS, supporting various chart types like bar charts, utilizing pyecharts and Apache ECharts for interactive visualizations.

Capabilities Granted for visualization

Generating interactive bar charts for data insights
Creating self-contained HTML reports with embedded JS
Visualizing data trends using Apache ECharts

! Prerequisites & Limits

  • Requires pyecharts and Apache ECharts libraries
  • Output limited to self-contained HTML with embedded JS
Labs Demo

Browser Sandbox Environment

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visualization

Install visualization, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
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Visualization Skill

Technology Stack

  • pyecharts: Python wrapper for Apache ECharts
  • Apache ECharts: JavaScript charting library
  • Output: Self-contained HTML with embedded JS

Chart Types Reference

Bar Charts

python
1from pyecharts.charts import Bar 2from pyecharts import options as opts 3 4chart = Bar() 5chart.add_xaxis(labels) 6chart.add_yaxis("Series Name", values) 7chart.set_global_opts( 8 title_opts=opts.TitleOpts(title="Chart Title"), 9 tooltip_opts=opts.TooltipOpts(trigger="axis"), 10 xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)), 11)

Line Charts

python
1from pyecharts.charts import Line 2 3chart = Line() 4chart.add_xaxis(dates) 5chart.add_yaxis("Actual", values, is_smooth=True) 6chart.add_yaxis("7-Day MA", moving_avg_7, is_smooth=True, linestyle_opts=opts.LineStyleOpts(type_="dashed"))

Pie Charts

python
1from pyecharts.charts import Pie 2 3chart = Pie() 4chart.add("", list(zip(labels, values))) 5chart.set_global_opts(legend_opts=opts.LegendOpts(orient="vertical", pos_left="left"))

Heatmaps

python
1from pyecharts.charts import HeatMap 2 3chart = HeatMap() 4chart.add_xaxis(x_labels) 5chart.add_yaxis("", y_labels, value=[[x, y, val], ...]) 6chart.set_global_opts( 7 visualmap_opts=opts.VisualMapOpts(min_=0, max_=max_val), 8)

Scatter Plots (for anomalies)

python
1from pyecharts.charts import Scatter 2 3chart = Scatter() 4chart.add_xaxis(dates) 5chart.add_yaxis("Cost", costs, symbol_size=10) 6# Add anomaly markers with different color/size

Critical: Browser Compatibility

Always convert to lists for JavaScript:

python
1# CORRECT 2chart.add_xaxis(df['column'].tolist()) 3chart.add_yaxis("Label", df['values'].tolist()) 4 5# WRONG - causes rendering issues 6chart.add_xaxis(df['column'].values) # numpy array 7chart.add_xaxis(df['column']) # pandas Series

Theme Options

Available themes in pyecharts:

  • macarons (default) - Colorful, professional
  • shine - Bright colors
  • roma - Muted, elegant
  • vintage - Retro feel
  • dark - Dark background
  • light - Light, minimal

Usage:

python
1from pyecharts.globals import ThemeType 2chart = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))

HTML Report Structure

python
1def generate_html_report(self, output_path: str, top_n: int = 10) -> str: 2 # Create all charts 3 charts = [ 4 self.create_cost_by_service_chart(top_n), 5 self.create_cost_by_account_chart(), 6 # ... more charts 7 ] 8 9 # Combine into page 10 page = Page(layout=Page.SimplePageLayout) 11 for chart in charts: 12 page.add(chart) 13 14 # Render to file 15 page.render(output_path) 16 return output_path

Formatting Numbers

python
1# Currency formatting in tooltips 2tooltip_opts=opts.TooltipOpts( 3 trigger="axis", 4 formatter="{b}: ${c:,.2f}" 5) 6 7# Axis label formatting 8yaxis_opts=opts.AxisOpts( 9 axislabel_opts=opts.LabelOpts(formatter="${value:,.0f}") 10)

Common Issues & Solutions

Empty Charts

  1. Check browser console for JS errors
  2. Verify .tolist() on all data
  3. Hard refresh (Ctrl+Shift+R)
  4. Check data exists in HTML source

Chart Too Small

python
1init_opts=opts.InitOpts(width="100%", height="400px")

Labels Overlapping

python
1xaxis_opts=opts.AxisOpts( 2 axislabel_opts=opts.LabelOpts(rotate=45, interval=0) 3)

Legend Too Long

python
1legend_opts=opts.LegendOpts( 2 type_="scroll", 3 orient="horizontal", 4 pos_bottom="0%" 5)

Testing Visualizations

bash
1# Test chart creation 2uv run pytest tests/test_visualizer.py -v 3 4# Regenerate example report 5uv run pytest tests/test_examples.py -v -s 6 7# View in browser 8open examples/example_report.html

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is visualization?

Perfect for Data Analysis Agents needing interactive chart generation capabilities with pyecharts and Apache ECharts. Visualization is a skill that generates interactive charts and graphs using technologies like pyecharts and Apache ECharts, enabling developers to create informative and engaging data visualizations.

How do I install visualization?

Run the command: npx killer-skills add T-Klug/aws-cur-report-generator/visualization. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for visualization?

Key use cases include: Generating interactive bar charts for data insights, Creating self-contained HTML reports with embedded JS, Visualizing data trends using Apache ECharts.

Which IDEs are compatible with visualization?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for visualization?

Requires pyecharts and Apache ECharts libraries. Output limited to self-contained HTML with embedded JS.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add T-Klug/aws-cur-report-generator/visualization. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use visualization immediately in the current project.

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