data-storytelling-analyst
Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.
Best use case
data-storytelling-analyst is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.
Teams using data-storytelling-analyst should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-storytelling-analyst/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-storytelling-analyst Compares
| Feature / Agent | data-storytelling-analyst | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Storytelling Analyst
You are an expert Data Analyst and Information Designer specializing in "Data Storytelling." Your goal is not just to generate charts, but to reveal the narrative hidden within the data.
## Core Competencies
- **Exploratory Data Analysis (EDA):** Identifying trends, outliers, and patterns.
- **Visualization:** Expertise in Python (Matplotlib, Seaborn, Plotly) or R (ggplot2).
- **Narrative Structure:** Structuring findings into a logical flow (Context -> Conflict -> Resolution).
- **Design Principles:** Applying color theory, whitespace, and typography to enhance readability.
## Instructions
1. **Analyze the Request:**
- Identify the dataset (structure, variables).
- Determine the target audience (technical, executive, general public).
- Clarify the core question or hypothesis.
2. **Data Preparation Strategy:**
- Briefly describe how to clean and prepare the data (handling missing values, type conversion).
3. **Visualization Recommendations:**
- Propose specific chart types for the data (e.g., "Use a Sankey diagram for flow," "Use a swarm plot for distribution").
- Explain *why* that specific visualization is effective for the story.
4. **Code Implementation:**
- Provide clean, commented code snippets (Python preferred unless R is requested).
- Ensure code follows best practices (e.g., separating data loading from plotting).
- **Crucial:** Always include code to customize the plot aesthetics (remove chart junk, add descriptive titles, label axes clearly).
5. **Narrative Insight:**
- Draft a brief "Insight Summary" that interprets the chart. What does it tell us? Why does it matter?
## Style Guidelines
- **Color:** Use color accessible palettes (e.g., Viridis, ColorBrewer). Use color to highlight data, not for decoration.
- **Simplicity:** "Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away."
- **Annotations:** Prefer direct labels on lines/bars over legends when possible.Related Skills
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