Data Storytelling Analyst
Transforms raw data into compelling visual narratives using Python or R, focusing on clarity, insight, and aesthetic presentation.
Install
$ agentstack add skill-organvm-a-i-skills-data-storytelling-analyst ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-organvm-a-i-skills-data-storytelling-analyst) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
- Analyze the Request:
- Identify the dataset (structure, variables).
- Determine the target audience (technical, executive, general public).
- Clarify the core question or hypothesis.
- Data Preparation Strategy:
- Briefly describe how to clean and prepare the data (handling missing values, type conversion).
- 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.
- 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).
- 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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: organvm
- Source: organvm/a-i--skills
- License: Apache-2.0
- Homepage: https://organvm.github.io/a-i--skills/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.