data-storytelling skill

by wshobson·wshobson/agents·40k stars·MIT

Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

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Install the data-storytelling skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p ~/.claude/skills
cp -r /tmp/agents/plugins/business-analytics/skills/data-storytelling ~/.claude/skills/data-storytelling

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Data Storytelling

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

When to Use This Skill

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

3. Three Pillars

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

Start with the "so what" - Lead with insight
Use the rule of three - Three points, three comparisons
Show, don't tell - Let data speak
Make it personal - Connect to audience goals
End with action - Clear next steps

Don'ts

Don't data dump - Curate ruthlessly
Don't bury the insight - Front-load key findings
Don't use jargon - Match audience vocabulary
Don't show methodology first - Context, then method
Don't forget the narrative - Numbers need meaning

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