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plot

Create publication-quality matplotlib plots and save PNG outputs.

Updated Aug 27, 2026
Search Tags:#plot#python#dashboard#visualization#matplotlib#data-visualization#plots
One-click install
npx skills add https://github.com/SpecForgeAI/deepagent-bot --skill plot-specforgeai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plot
Source: https://github.com/SpecForgeAI/deepagent-bot/tree/main/skills/plot
Command: npx skills add https://github.com/SpecForgeAI/deepagent-bot --skill plot-specforgeai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the creation of publication-quality plots from data using matplotlib, reducing the manual effort required to generate professional visualizations.

Core Features & Use Cases

  • Line plots, scatter plots, bar charts, histograms
  • Boxplots, violin plots, heatmaps
  • Subplots and multi-panel figures
  • Custom styling (colors, labels, titles, legends, grids)
  • Curve overlays and annotations
  • Save as PNG (default) or SVG
  • Output location: All plots are saved to output/ (OUTPUT_DIR) and accessible at /output/<filename>

Quick Start

Provide Python code that uses matplotlib to generate the desired plot and the optional filename to save to the output directory.

Frequently Asked Questions about plot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-quality plots from data using Python and matplotlib?▼

To generate publication-quality plots, this Skill automates matplotlib visualization creation from your data. It applies custom styling for colors, labels, and legends, then saves outputs directly as PNG or SVG files to a defined output directory.

Can I create multi-panel figures and subplots for research reporting?▼

Yes, you can create multi-panel figures and subplots for research reporting. The Skill supports configuring subplots alongside various chart types like scatter plots, boxplots, and heatmaps within a single visualization output.

What Python environment do I need to render and save data visualizations?▼

You need a Python environment with matplotlib and numpy available to render and save data visualizations. This setup allows the Skill to execute your plotting code and write the resulting image files to the specified output directory.

Does this approach work for building dashboards across Python notebooks and scripts?▼

Yes, this approach works for dashboard generation across Python notebooks, scripts, and applications. It automates the creation of various plots, including histograms and violin plots, outputting static image files for your dashboard interface.

What are the limitations of using matplotlib for automated data visualization?▼

A limitation of using matplotlib for automated data visualization is that it outputs static PNG or SVG files rather than interactive graphics. Additionally, you must provide the plotting code and ensure numpy is installed in your environment.

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