data-storytelling

Data Community

Repo stars 36,312
Author repo agents

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “When to Use This Skill”, “Core Concepts”, “1. Story Structure”, “2. Narrative Arc”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name data-storytelling directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “When to Use This Skill / Core Concepts / 1. Story Structure” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Data
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @wshobson · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Shell exec
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview data-storytelling.preview

# Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

SKILL.md SKILL.md

# 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

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> When to Use This Skill → Core Concepts → 1. Story Structure → 2. Narrative Arc → 3. Three Pillars → Detailed patterns and worked examples
terms    -> Data · Narrative · Visuals · Start with the "so what" · Use the rule of three · Show, don't tell · Make it personal · End with action
files/cmd -> references/details.md
body sha256 -> ce3a472cf394