data-storytelling
Build compelling data-driven stories using structured narratives and visual frameworks for presentations and reports.
Install
mkdir -p .claude/skills/data-storytelling && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/505" && unzip -o skill.zip -d .claude/skills/data-storytelling && rm skill.zipInstalls to .claude/skills/data-storytelling
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.Key capabilities
- →Applies setup-conflict-resolution narrative arcs to data
- →Drafts data-driven reports using a hook-context-climax structure
- →Curates metrics into a three-pillar evidence framework
- →Identifies actionable next steps from raw trends
- →Adapts language to suit non-technical stakeholders
How it works
It maps analytical findings into a predefined story arc structure and applies a three-pillar model to ensure evidence, meaning, and visual clarity.
Inputs & outputs
When to use data-storytelling
- →Create a quarterly business review
- →Present data insights to stakeholders
- →Build investor presentations
- →Write data-driven project reports
About this skill
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
When not to use it
- →Exploratory data analysis before finding a narrative
- →Raw statistical reporting without intended audience impact
- →Situations requiring pure data dumping
Limitations
- →Requires pre-processed data to identify insights
- →Risk of over-simplification if context is missing
- →Narrative strength depends on the quality of underlying insights
How it compares
It structures data findings into a persuasive narrative arc rather than simply listing charts or summary statistics.
Compared to similar skills
data-storytelling side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| data-storytelling (this skill) | 47 | 2mo | No flags | Intermediate |
| csv-data-summarizer | 15 | 9mo | Review | Beginner |
| umap-learn | 6 | 2mo | Review | Intermediate |
| data-visualization | 25 | 5mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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