What does this skill do?

The Data Analysis & Jupyter skill gives an agent expert guidance for data analysis, visualization, and notebook development using the core Python data-science stack.

It favors readable, reproducible, vectorized code — pandas transformations and method chaining, seaborn and matplotlib visualization, and well-structured notebooks with logical cell order.

It also covers numpy best practices, data-quality checks and validation, performance optimization, and statistical testing with scipy.

  • Transforms and aggregates data with pandas

  • Builds clear, accessible matplotlib and seaborn visuals

  • Structures reproducible Jupyter notebooks

  • Runs statistical tests and validates data quality

How it works

1

Explore the data

The agent begins with exploratory data analysis, documenting assumptions and data-quality issues.

2

Transform with pandas

It uses vectorized pandas operations, method chaining, and explicit selection with loc and iloc for clean transformations.

3

Visualize

It builds informative matplotlib and seaborn plots with labels, titles, and accessible, color-blind-safe palettes.

4

Structure and validate

It organizes a reproducible notebook with markdown sections, validates data types and ranges, and runs statistical tests where appropriate.

Best use cases for this skill

  • Exploratory analysis: Profile and transform a dataset with pandas.

  • Visualization: Create statistical plots with seaborn and matplotlib.

  • Reproducible notebooks: Structure a Jupyter notebook others can rerun.

  • Statistical testing: Run hypothesis tests and compute confidence intervals.

Frequently asked questions

What does the Data Analysis & Jupyter skill do?

It provides expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy, emphasizing reproducible workflows.

When should an agent use it?

Use it for analyzing data in Python, building visualizations, or developing reproducible Jupyter notebooks.

What does it emphasize?

Readable, reproducible code — vectorized pandas and numpy operations, PEP 8 style, logical notebook cell order, and kernel-restart reproducibility checks.

Which libraries does it use?

pandas, numpy, matplotlib, seaborn, scipy, and scikit-learn, with jupyter for notebook development.

Use from source

Ask your Buda agent to install this community skill.

Copy this prompt into your own Buda agent. It includes the source repository and install command so the agent can complete the setup in natural language.

Prompt to send to your Buda agent
Please install the "Data Analysis & Jupyter Skill" skill into this Buda agent.

Source repository: https://github.com/Mindrally/skills
Install command: npx skills add Mindrally/skills --skill data-analysis-jupyter -y -a universal

Please run the install command, verify the skill is available, and tell me any setup notes or follow-up steps from the source repository.
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Install workflow

  1. 1Copy the prompt
  2. 2Send it to your Buda agent
  3. 3Done