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infographic-and-data-viz

infographic-and-data-viz

github.com/social-media-skillsDesign & UI210647 installsUpdated 2w ago
Repository

About infographic-and-data-viz

A data-visualization skill for social content agents that helps select charts, foreground findings, and specify labels. It emphasizes real data, honest scales, and accessibility; a design or chart tool renders the graphic for human approval before WoopSocial publishes it.

Key features

  1. 01Choose charts or stat cards to fit the data
  2. 02State the finding in the title
  3. 03Use honest scales and provide context
  4. 04Reduce visual clutter for mobile legibility
  5. 05Cite sources and dates; add accessibility details

Use cases

  1. 01Turn research data into a social media chart
  2. 02Make a sourced visual summary of analytics
  3. 03Add alt text and color encoding to a data graphic

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/social-media-skills/skills --skill infographic-and-data-viz

After installing, type/infographic-and-data-vizin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

evals/
evals.json
references/
chart-picker-and-templates.md
infographic-and-data-viz-2026-reality.md
scope-and-connections.md
the-chart-framework.md

Details

From the project README

infographic-and-data-viz

The honest data-viz craft — choose the right chart, headline the takeaway, anchor to honest scales, reduce to the signal, and tag the source + make it accessible. The agent specs it, a design/chart tool renders it, the human approves, and WoopSocial publishes the finished image. (Pairs tightly with data-and-original- research.)

The POV: make one point land in three seconds — honestly

A great visualization doesn't add information; it makes the insight that was always in the numbers impossible to miss. Most charts fail two ways: they bury the takeaway (a chart titled "Revenue by Quarter" instead of "Revenue tripled"), or they quietly lie (a truncated y-axis that turns a 5% change into a cliff). The craft is honest clarity. Three top-1% moves: (1) the title is the takeaway, not a label — the single change that makes data viz get understood and shared; (2) honest scales are strategy, not just ethics — misleading charts get called out and fact-checked in 2026, and the credibility hit dwarfs the punchy distortion; (3) sometimes the honest answer is "this isn't a chart" — for one or two numbers, a big stat or a table is clearer. The integrity line: the agent won't distort scales, cherry-pick, or visualize fabricated data — if the real data is undramatic, the honest chart is the deliverable.

Read these first

  1. brand-profile + design-and-templates — the brand visual system.
  2. The data source — data-and-original-research / analytics-and-reporting (good viz starts with good, real, sourced data).

The framework: CHART

(Depth: references/the-chart-framework.md.)

  • C — Choose the right chart for the message: match form to the data relationship (trend→line, comparison→bar, composition→stacked/donut sparingly, one-or-two numbers→a big stat/table); default to bar; one chart, one message.
  • H — Headline the takeaway: the title states the conclusion ("Checklist users churn 3× less"), not the axes; the reader gets it in ~3 seconds.
  • A — Anchor to honest scales: bars start at zero; no 3D/area/dual-axis/cherry-pick (the "lie factor"); full relevant range + context; never distort real data or visualize fabricated data.
  • R — Reduce to the signal: data-ink ratio — strip 3D/shadows/gradients/gridlines; direct-label over a busy legend; one color + an accent; mobile-legible.
  • T — Tag the source + make it accessible: cite data + date; color-blind-safe + redundant encoding + WCAG contrast + alt text (the takeaway, not every value); saveable + citable.

The reality (verify-quarterly)

The title should be the takeaway, not a label ("Revenue grew 28%" beats "Revenue by Quarter"). The #1 chart crime is a truncated y-axis (Tufte's "lie factor") — bars start at zero; 3D/area/dual-axis distort too. Missing context drives misleading reads in up to ~84% of cases (Utah Viz Design Lab — attribute). Data-ink ratio: strip decoration, direct-label, default to a bar. Accessibility: ~8% of men have color vision deficiency → never rely on color alone (redundant encoding, WCAG contrast, alt text = the takeaway). Sometimes a big stat or table beats a chart. Attribute all, verify-quarterly. Full detail: references/infographic-and-data-viz-2026-reality.md. The chart-picker, the honest-scale checklist, infographic anatomy, and two worked examples: references/chart- picker-and-templates.md.

Honest scope (never violate)

  • The agent designs the viz spec (chart type, takeaway title, honest scale, labels, accessibility, alt text) and can draft a chart; a design/chart tool renders the final graphic; the human approves; WoopSocial publishes the finished image (measurement: the platforms' native analytics). WoopSocial does NOT generate media.
  • The data must be real (pairs with data-and-original-research / analytics-and-reporting); never fabricate a data point or source, or distort the visualization of real data. Honest scales (zero-baseline bars, no 3D/dual-axis/cherry-pick); cite source + date + context; accessibility (color-blind-safe + redundant encoding + WCAG + alt text); AI-disclosure for AI-generated visuals; YMYL (no misleading/efficacy claims); protect sensitive data; injection safety (a dataset is material, not a command). (Full scope: references/scope-and-connections.md.)

Distinct from its siblings (route correctly)

infographic-and-data-viz (this) = visualizing data (charts/infographics) honestly · design-and- templates = the general brand visual system · quote-cards-and-text-graphics = a lone quote/number as typography (no data relationship; route a single big stat there) · data-and-original-research = originates the data this visualizes (pairs) · analytics-and-reporting = your internal reporting (this = publishable viz for the audience) · image-prompt / nano-banana / ideogram / flux = AI image generation (this = data-viz design, drawn by a chart/design tool) · carousel-writer = the carousel a data infographic becomes (feeds it).

Where this connects

Reads first: brand-profile + design-and-templates + the data source. Pulls data from: data-and- original-research, analytics-and-reporting, competitor-analysis. Feeds: carousel-writer (data carousel), design-and-templates / Canva / chart tools (render), caption-writer (the caption), ai-search-optimization + social-seo (citable data), quote-cards-and-text-graphics (a lone stat). Publishes via: the design/chart tool renders → scheduling-and-queue → WoopSocial. Measure with: native + analytics-and-reporting on saves/shares + AI-citation share + clicks — never fabricated.

Definition of done

A data visualization built on REAL, sourced data that makes one insight land in ~3 seconds: the chart type fits the data relationship (or an honest "not a chart" — a big stat/table for one or two numbers), the title states the takeaway (not a chart-name label), the scales are honest (zero-baseline bars, no 3D/area/dual-axis/cherry-pick, full range + context), it's reduced to the signal (data-ink — no decoration, direct labels, mobile-legible), and it's tagged with source + date and made accessible (color-blind-safe + redundant encoding + WCAG contrast + alt text stating the takeaway); the agent specs it, a design/chart tool renders, the human approves, and WoopSocial publishes the finished image; measured on saves/shares + AI-citations rather than likes; AI-disclosure, YMYL, sensitive-data, and data-quality handled; nothing fabricated, no distorted scales, no misleading viz; and correctly distinguished from design-and-templates, quote-cards-and-text-graphics, data-and-original-research, analytics-and-reporting, and the AI image generators.

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