Data Storytelling

Turns raw data into narratives using structured frameworks like problem-solution, trend analysis, and comparison stories. Built around three pillars: data as evidence, narrative for meaning, and visuals for clarity. You get concrete templates for executive summaries, progressive reveal techniques, and story arcs that follow setup-conflict-resolution patterns. Most useful when you're presenting analytics to non-technical audiences or building business reviews where the data needs to drive decisions rather than just inform. The implementation playbook includes Python visualization examples and annotation techniques. It's opinionated about structure, which helps if you're staring at spreadsheets wondering how to make them matter to someone who doesn't care about your pivot tables.

skills.sh installs: 328
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill data-storytelling --agent claude-code

Installs into .claude/skills of the current project.

Files

Data Storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

Do not use this skill when

The task is unrelated to data storytelling
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
Use the frameworks and examples below; verify calculations against the supplied source data.

Use this skill when

Presenting analytics to executives
Creating quarterly business reviews
Building investor presentations
Writing data-driven reports
Communicating insights to non-technical audiences
Making recommendations based on data

Core Concepts

1. Story Structure

Setup → Conflict → Resolution

Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations

2. Narrative Arc

1. Hook: Grab attention with surprising insight
2. Context: Establish the baseline
3. Rising Action: Build through data points
4. Climax: The key insight
5. Resolution: Recommendations
6. Call to Action: Next steps

SKILL.md on GitHub