# Data Storytelling

数据科学家与产品经理在向高管汇报或制作投资者演示时，当面临数据枯燥难懂的痛点，使用此技能可套用“Setup-Conflict-Resolution”叙事框架，自动生成结合可视化...

- Repository: https://github.com/anbeime/skill (9005 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add anbeime/skill --skill data-storytelling -g -a claude-code -y`
Codex: `npx skills add anbeime/skill --skill data-storytelling -g -a codex -y`
Cursor: `npx skills add anbeime/skill --skill data-storytelling -g -a cursor -y`
Gemini CLI: `npx skills add anbeime/skill --skill data-storytelling -g -a gemini-cli -y`

## Description

数据科学家与产品经理在向高管汇报或制作投资者演示时，当面临数据枯燥难懂的痛点，使用此技能可套用“Setup-Conflict-Resolution”叙事框架，自动生成结合可视化与业务上下文的洞察报告，让非技术受众秒懂结论并驱动决策。

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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     |

## Story Frameworks

### Framework 1: The Problem-Solution Story

```markdown
# Customer Churn Analysis

## The Hook

"We're losing $2.4M annually to preventable churn."

## The Context

- Current churn rate: 8.5% (industry average: 5%)
- Average customer lifetime value: $4,800
- 500 customers churned last quarter

## The Problem

Analysis of churned customers reveals a pattern:

- 73% churned within first 90 days
- Common factor: < 3 support interactions
- Low feature adoption in first month

## The Insight

[Show engagement curve visualization]
Customers who don't engage in the first 14 days
are 4x more likely to churn.

## The Solution

1. Implement 14-day onboarding sequence
2. Proactive outreach at day 7
3. Feature adoption tracking

## Expected Impact

- Reduce early churn by 40%
- Save $960K annually
- Payback period: 3 months

The rest: https://github.com/anbeime/skill/blob/HEAD/skills/data-storytelling/data-storytelling/SKILL.md