Can this skill help with Python data visualization?

Yes, it includes specific techniques and code patterns for libraries like Matplotlib to create annotated, high-impact charts that highlight key insights rather than just raw data.

How does it help with non-technical audiences?

It provides templates and writing techniques that strip away jargon, focus on the 'so what' of the data, and use headlines that emphasize business impact and actionable insights.

What is the Data Storytelling skill for Claude Code?

It is a specialized plugin that provides Claude with the frameworks and best practices needed to turn raw analytical data into structured, persuasive narratives for business stakeholders.

Which storytelling frameworks are included?

The skill includes the Problem-Solution Story, the Trend Story for performance tracking, and the Comparison Story for market or opportunity analysis.

Data Storytelling & Analytics

Name: Data Storytelling & Analytics
Author: EricGrill

byEricGrill

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Analytics & Monitoring

Transforms raw analytical data into persuasive narratives and executive-ready presentations using proven storytelling frameworks.

About

The Data Storytelling skill is a specialized extension for Claude Code designed to help analysts and developers communicate complex data insights effectively to stakeholders. It provides structured narrative frameworks—such as the Problem-Solution and Trend models—to turn dry metrics into compelling stories with clear calls to action. By incorporating advanced visualization techniques, progressive reveal strategies, and persuasive writing patterns, this skill ensures that technical findings lead to informed business decisions and organizational alignment.

Key Features

2 GitHub stars
Narrative arc structuring (Setup, Conflict, Resolution)
Progressive reveal and annotation techniques for data visualization
Standardized business frameworks for problem-solving and trend analysis
Executive-ready presentation templates and report structures
Persuasive headline and transition phrase generation for non-technical audiences

Use Cases

Drafting data-driven investor presentations that highlight market opportunities and ROI.
Converting complex technical metrics into quarterly business reviews (QBRs) for executives.
Communicating churn analysis and product adoption insights to product management teams.

About

Key Features

2 GitHub stars
Narrative arc structuring (Setup, Conflict, Resolution)
Progressive reveal and annotation techniques for data visualization
Standardized business frameworks for problem-solving and trend analysis
Executive-ready presentation templates and report structures
Persuasive headline and transition phrase generation for non-technical audiences

Use Cases

Drafting data-driven investor presentations that highlight market opportunities and ROI.
Converting complex technical metrics into quarterly business reviews (QBRs) for executives.
Communicating churn analysis and product adoption insights to product management teams.