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plotting

Create bar charts, box plots, and time-series plots from SWARM simulation data.

39|4|Updated Feb 3, 2026
Search Tags:#data analysis#simulation#visualization#swarm#plotting
One-click install
npx skills add https://github.com/swarm-ai-research/swarm --skill plotting-swarm-ai-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plotting
Source: https://github.com/swarm-ai-research/swarm/tree/main/bench/skills/plotting
Command: npx skills add https://github.com/swarm-ai-research/swarm --skill plotting-swarm-ai-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, pandas, numpy, seaborn, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the generation of publication-quality visualizations from SWARM simulation data, streamlining the analysis and presentation of results.

Core Features & Use Cases

  • Visualization Generation: Automates the creation of standard visualizations like bar charts, box plots, and time-series plots.
  • Data Analysis: Provides tools to analyze sweep results and time-series history from SWARM simulations.
  • Use Case: For researchers and analysts working with SWARM, this Skill can quickly produce visualizations of welfare, agent payoffs, and toxicity rates, aiding in the interpretation of simulation outcomes.

Quick Start

Generate a plot of welfare by governance configuration for the provided SWARM simulation data using the plotting skill.

Frequently Asked Questions about plotting

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

FAQPage Schema
How do I visualize SWARM simulation data for welfare and agent payoffs?▼

You can visualize SWARM simulation data by automating the creation of bar charts, box plots, and time-series plots to analyze welfare, agent payoffs, and toxicity rates. This streamlines the interpretation of simulation outcomes.

What Python libraries do I need to plot simulation time-series data?▼

To plot simulation time-series data, you need matplotlib, pandas, numpy, and seaborn. These dependencies handle the data manipulation and visualization required to generate publication-quality charts from your results.

Can I generate publication-quality bar charts from simulation sweep results?▼

Yes, you can generate publication-quality bar charts from simulation sweep results. The skill automates visualization creation for post-processing, specifically analyzing sweep results and time-series history from SWARM simulations.

How do I create box plots for toxicity rates in SWARM post-processing?▼

You create box plots for toxicity rates by applying the plotting skill to your SWARM post-processing data. It automates the generation of standard visualizations, aiding in the interpretation of welfare and toxicity outcomes.

What is the best way to analyze SWARM simulation time-series history?▼

The best way to analyze SWARM simulation time-series history is using automated visualization tools. This skill generates time-series plots, bar charts, and box plots to help researchers quickly interpret agent payoffs and welfare data.

Does this plotting skill work without pandas and numpy installed?▼

No, this plotting skill does not work without pandas and numpy. It requires matplotlib, pandas, numpy, and seaborn to be installed for the data manipulation and visualization processes needed to create the charts.

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