data-storytelling
Makes your agent turn data into easy-to-understand stories. It's useful for presenting complex information clearly and persuasively.
What it does
This tool turns raw data into interesting stories to help make decisions. It uses structure, context, and visuals to make numbers easy to understand.
When to use it
- ✓When presenting analytics to bosses.
- ✓When creating quarterly business reports.
- ✓When making presentations for investors.
Example of what to ask your AI
“Turn this sales data into a presentation for investors.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool organizes your data into a story structure with a beginning, middle, and end. It uses charts and simple explanations to show what the numbers mean and what to do next.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill data-storytellingOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/business-analytics/skills/data-storytelling/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from 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
| 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
FAQ
What is 'Setup → Conflict → Resolution'?
It's a way to tell a story: 'Setup' is the initial context, 'Conflict' is the problem or opportunity, and 'Resolution' are the solutions and conclusions.
What are the 'Three Pillars'?
These are the essential elements: Data (the numbers), Narrative (the meaning), and Visuals (the charts) to tell the story.
What does 'Don't data dump' mean?
It means not showing all the data at once. Choose only the most important data for your story.
Details
Original description by the author
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4818
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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