data-storytelling
wshobson/agents
Transform data into compelling narratives that drive decisions and inspire action.
What is data-storytelling?
Data storytelling combines data, narrative, and visuals to communicate insights effectively to stakeholders. Use this skill when presenting analytics to executives, creating reports, or building presentations where you need to turn raw data into actionable insights.
- Structure narratives using setup-conflict-resolution framework
- Build compelling arcs with hooks, context, rising action, climax, and call-to-action
- Balance three pillars: data evidence, narrative meaning, and visual clarity
- Curate data ruthlessly to avoid overwhelming audiences
- Connect insights to audience goals and business outcomes
- Lead with key findings rather than methodology or data dumps
How to install data-storytelling
npx skills add https://github.com/wshobson/agents --skill data-storytellingHow to use data-storytelling
- 1.Start by identifying your key insight (the 'so what')
- 2.Establish context and baseline for your audience
- 3.Build your narrative arc: hook, context, rising action, climax, resolution
- 4.Select 2-3 data points that support your main insight (rule of three)
- 5.Create visuals that clarify rather than decorate
- 6.End with clear next steps and call to action
- 7.Match your vocabulary and complexity to your audience
Use cases
- Presenting quarterly business reviews to executives
- Building investor pitch decks with data-driven recommendations
- Writing reports that communicate analytics to non-technical stakeholders
- Creating executive summaries that highlight key insights and next steps
- Communicating opportunities or problems identified through data analysis
- Business analysts presenting to leadership
- Data professionals communicating with non-technical audiences
- Product managers building cases for decisions
- Executives preparing investor presentations
- Anyone translating data into actionable recommendations
data-storytelling FAQ
Data storytelling adds narrative structure and meaning to numbers. Instead of showing a spreadsheet, you establish context, highlight the key insight, and guide the audience to a conclusion with clear next steps.
Curate ruthlessly. Include only data points that directly support your main insight. Use the rule of three—three comparisons, three trends, or three supporting points—to keep it memorable and avoid overwhelming your audience.
No. Lead with your key insight and context first, then explain methodology if the audience asks. Burying methodology upfront loses attention and obscures the story.
Before presenting, understand what your audience cares about. Frame your insights in terms of their priorities—revenue impact, risk reduction, opportunity, or strategic alignment—not just the data itself.
Full instructions (SKILL.md)
Source of truth, from wshobson/agents.
name: data-storytelling description: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
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
| Pillar | Purpose | Components |
|---|---|---|
| Data | Evidence | Numbers, trends, comparisons |
| Narrative | Meaning | Context, causation, implications |
| Visuals | Clarity | Charts, diagrams, highlights |
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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