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RealTaskRealTask/MAX-DESIGN/skills/data-visualization/SKILL.md

data-visualization

Skill4 starsChanged 23 days agoMITView on GitHub

Select, design, label, and verify charts and data displays that help users interpret evidence accurately and accessibly.

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 40 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use for analytics dashboards, reports, metrics, charts, tables, trend comparisons, financial summaries, monitoring views, or any interface …

    line 10
  2. Distinctive:

    Treat these as starting heuristics, not rigid rules.

    line 27
  3. Distinctive:

    Use accurate scales, meaningful zero baselines when needed for comparison, consistent units, explicit ranges, and clear labels.

    line 31
  4. Distinctive:

    Test legend toggles, tooltips, sorting, filters, resizing, small screens, long labels, large values, negative values, and missing points.

    line 47

“Common” is measured: kept by at least 6 in 10 of the 40 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Data visualization

When to use

Use for analytics dashboards, reports, metrics, charts, tables, trend comparisons, financial summaries, monitoring views, or any interface that presents quantitative information for decisions.

Workflow

1. Clarify the decision

Identify the question the reader must answer, the metric definition, units, population, time range, aggregation, source, and relevant caveats. Do not start by choosing a chart. If the data cannot answer the question, state the limitation.

2. Match encoding to the task

  • Compare categories: usually a bar or dot plot with a clear common baseline.
  • Show change over ordered time: line charts when continuity is meaningful; bars for discrete periods when comparison is clearer.
  • Show composition: stacked bars for comparisons, or a part-to-whole chart only when the number of parts and comparisons make it legible.
  • Show distributions: histogram, box plot, or dot plot depending on sample size and audience.
  • Show relationships: scatter plot when both variables and their units matter.
  • Show exact values or dense detail: a sortable, labeled table may be better than a chart.

Treat these as starting heuristics, not rigid rules. Avoid 3D distortion, decorative chart junk, and dual axes unless the reasoning and scale are explicit.

3. Encode faithfully

Use accurate scales, meaningful zero baselines when needed for comparison, consistent units, explicit ranges, and clear labels. Do not truncate or normalize an axis in a way that exaggerates differences. Distinguish missing data from zero. Identify estimates, forecasts, and uncertainty.

4. Provide context and hierarchy

State the metric and unit, date range, relevant baseline, and what changed. Add concise annotations only when they support an interpretation. Avoid presenting a correlation as causation or a metric without its denominator or definition when these change its meaning.

5. Design for accessibility

Use direct labels and patterns or text alongside color when categories must be distinguished. Ensure sufficient contrast, readable tick labels, keyboard access for interactive charts, and a concise textual summary or equivalent data table for important information. Do not force people to hover to obtain essential values.

6. Design dashboard states

Define initial, loading, no-data, filtered-empty, partial-data, stale-data, error, and refreshed states. Make active filters and date windows visible. Explain data freshness and avoid fake live values or synthetic metrics represented as real.

7. Validate behavior and performance

Test legend toggles, tooltips, sorting, filters, resizing, small screens, long labels, large values, negative values, and missing points. Check that data remains interpretable when animation is disabled. Consider rendering cost for large data sets and avoid needless re-rendering.

Quality gates

  • Chart type matches the user question and underlying data.
  • Units, time range, definitions, scale, and data provenance are understandable.
  • Encodings do not mislead; missing values are not confused with zero.
  • Important information does not depend on color or hover alone.
  • A text summary or equivalent table exists when needed.
  • Loading, stale, empty, filtered, and error states are represented accurately.
  • Conclusions do not exceed what the data supports.

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  • Other versions46 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 46

    1. anthropics/knowledge-work-plugins★ 34ksame name
    2. langchain-ai/deepagents★ 31ksame name
    3. openai/plugins★ 7.4ksame name
    4. vivy-yi/xiaohongshu-skills★ 455same name
    5. NTCoding/claude-skillz★ 342same name
    6. didixuxu/didi-skills★ 83same name
    7. launchapp-dev/animus-cli★ 39same name
    8. xjtulyc/awesome-rosetta-skills★ 34same name
    9. kensaurus/cursor-kenji★ 9same name
    10. kensaurus/skills★ 9same name

    36 more not listed

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • Used alongsideProjects with it also use case-study-writing, interview-script and 10 more.

    Other files people use alongside this one.

    Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    • MicrosoftSkill

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      Also in 3 projects that use this

  • In this repository13 other files it gives its agents.

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