skills/visual-storytelling-design/SKILL.md
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.
npx skillsauth add lyndonkl/claude visual-storytelling-designInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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
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
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
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
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.
Choose this when: Starting a data story and need to define the narrative arc and opening strategy.
→ Go to Narrative Techniques — Sections 1-2
Choose this when: Adding annotations to guide interpretation of existing charts and visualizations.
→ Go to Narrative Techniques — Section 3
Choose this when: Building web-based progressive revelation experiences.
→ Go to Narrative Techniques — Section 4
Choose this when: Providing context, baselines, comparisons, and visual metaphors.
→ Go to Narrative Techniques — Sections 5-6
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.
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.