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visual-storytelling-design

Transforms data into compelling visual narratives by applying narrative structure, annotation techniques, scrollytelling patterns, and honest framing to data journalism, presentations, and infographics. Use when creating data-driven articles or reports, designing infographics with narrative, building scrollytelling experiences, annotating charts to guide interpretation, or when user mentions data storytelling, presentation design, annotated chart, narrative visualization.

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含まれるファイル(4)

  • SKILL.md4.8 KB
  • resources/evaluators/rubric_visual_storytelling.json4.0 KB
  • resources/narrative-techniques.md7.2 KB
  • resources/storytelling-patterns.md7.0 KB

SKILL.md(原文)

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Visual Storytelling Design

Table of Contents


Core principle: Structuring data as narrative (Context → Problem → Evidence → Insight) aids comprehension and retention. Annotations guide attention, progressive disclosure reveals complexity gradually, and framing provides context for accurate interpretation.

Related skills: Use cognitive-design for cognitive principles, d3-visualization for D3.js implementation, design-evaluation-audit for systematic evaluation, cognitive-fallacies-guard for integrity checks.


Story Design Workflow

Time: 1-2 hours

Copy this checklist and track your progress:

Story Design Progress:
- [ ] Step 1: Define Narrative
- [ ] Step 2: Choose Structure
- [ ] Step 3: Apply Cognitive Techniques
- [ ] Step 4: Review for Clarity & Integrity

Step 1: Define Narrative

Determine the story arc: What's the context? What's the question/problem? What data answers it? What's the insight? Choose an opening strategy: lead with human impact, surprising finding, or visual.

Resource: Narrative Techniques — Narrative Structure section

Step 2: Choose Structure

Select a template and pattern that fits your story type, audience, and medium. Options include step-by-step article, magazine style, annotated chart, interactive exploration, or presentation deck.

Resource: Storytelling Patterns — Templates and Decision Matrix

Step 3: Apply Cognitive Techniques

Add annotations (callouts, arrows, shaded regions, direct labels). Apply framing with baselines, comparisons, and denominator clarity. Use scrollytelling for progressive revelation if web-based. Consider visual metaphors.

Resource: Narrative Techniques — Annotations, Scrollytelling, Framing sections

Step 4: Review for Clarity & Integrity

Verify the story is honest (no cherry-picking, balanced framing), clear (insight obvious in 5 seconds), and complete (sources cited, limitations noted). Use design-evaluation-audit for systematic evaluation and cognitive-fallacies-guard for integrity verification.


Path Selection Menu

Path 1: Build Narrative Structure

Choose this when: Starting a data story and need to define the narrative arc and opening strategy.

→ Go to Narrative Techniques — Sections 1-2


Path 2: Master Annotation

Choose this when: Adding annotations to guide interpretation of existing charts and visualizations.

→ Go to Narrative Techniques — Section 3


Path 3: Design Scrollytelling

Choose this when: Building web-based progressive revelation experiences.

→ Go to Narrative Techniques — Section 4


Path 4: Apply Framing & Metaphors

Choose this when: Providing context, baselines, comparisons, and visual metaphors.

→ Go to Narrative Techniques — Sections 5-6


Quick Reference

5 Storytelling Principles

  1. Lead with insight, not topic — Title: "Remote workers report 23% higher satisfaction" not "Remote work survey results"
  2. Annotate the insight — Don't make readers discover it; point it out with callouts
  3. Provide context — Baselines, historical comparisons, denominators for every percentage
  4. One change at a time — Scrollytelling: highlight OR annotate, not both simultaneously
  5. Be honest — Show full data, acknowledge limitations, avoid cherry-picking

Guardrails

Scope: This skill provides narrative structure, annotation techniques, scrollytelling patterns, framing guidance, story templates, and quality checklists for data storytelling. It does not implement code, evaluate general usability, teach cognitive theory, or check for misleading patterns.

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日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

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