What chart types does this skill support?

It supports a wide range of visualization families including trends (line, area), comparisons (bar, slope), distributions (histogram, violin), relationships (scatter, bubble), and compositions (treemap, sankey).

How does it help with narrative reporting?

The skill uses a structured 'Headline → Pattern → Context → Meaning → Action' framework to ensure reports aren't just descriptive but provide strategic, actionable advice.

Is it suitable for presenting data to executives?

Absolutely. It focuses on clarity, removing 'chart junk,' and leading with high-level insight headlines that make the core findings obvious at a glance.

Does this skill follow data design best practices?

Yes, it incorporates perceptual principles such as prioritizing position over area, using colorblind-safe palettes, and ensuring appropriate Y-axis scaling for different chart types.

Visualization Choice & Reporting

Name: Visualization Choice & Reporting
Author: lyndonkl

bylyndonkl

•

Data Science & ML

Matches data questions to optimal chart types and generates narrated reports that turn raw metrics into actionable business insights.

About

This skill bridges the gap between raw data analysis and effective communication by guiding users through a structured workflow for data visualization. It selects the most effective chart type based on the specific analytical question—whether identifying trends, comparing categories, or exploring distributions—and applies perceptual best practices to ensure clarity and accessibility. By following a structured 'Headline-Pattern-Context-Meaning-Action' framework, it transforms complex datasets into clear, annotated stories that help stakeholders understand not just what the data says, but exactly what strategic steps should be taken next.

Key Features

8 GitHub stars
Perceptual Best Practices: Implements design principles including colorblind-safe palettes, axis scaling, and reduced chart junk.
Action-Oriented Reporting: Recommends specific next steps and business actions based on identified data signals.
Question-Data Profiling: Analyzes data types, granularity, and dimensions to ensure the visualization handles data volume correctly.
Intelligent Chart Selection: Matches analytical goals to optimal visual forms like Treemaps, Sankey diagrams, or Box plots.
Structured Narrative Framework: Generates insight-first headlines and context-rich interpretations of complex data patterns.

Use Cases

Product Performance: Visualizing A/B test results and feature adoption rates to guide engineering and product roadmaps.
Business Analytics: Creating monthly revenue and churn reports with specific growth recommendations for stakeholders.
Operational Monitoring: Transforming technical KPIs and uptime metrics into annotated reports for executive review.

About

Key Features

8 GitHub stars
Perceptual Best Practices: Implements design principles including colorblind-safe palettes, axis scaling, and reduced chart junk.
Action-Oriented Reporting: Recommends specific next steps and business actions based on identified data signals.
Question-Data Profiling: Analyzes data types, granularity, and dimensions to ensure the visualization handles data volume correctly.
Intelligent Chart Selection: Matches analytical goals to optimal visual forms like Treemaps, Sankey diagrams, or Box plots.
Structured Narrative Framework: Generates insight-first headlines and context-rich interpretations of complex data patterns.

Use Cases

Product Performance: Visualizing A/B test results and feature adoption rates to guide engineering and product roadmaps.
Business Analytics: Creating monthly revenue and churn reports with specific growth recommendations for stakeholders.
Operational Monitoring: Transforming technical KPIs and uptime metrics into annotated reports for executive review.