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/display-quantitative-information

@3323bc9
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Design, critique, or implement quantitative graphics, scientific figures, dashboards, and evidence tables. Use for display choice, scale/encoding integrity, uncertainty, and accessible chart handoff. Do not use for decorative illustration or general data cleaning without a quantitative display.

Use this Skill: https://skilld.dev/gh/tristanmanchester/agent-skills/display-quantitative-information

Nothing lands on disk. Nothing to clean up.

Fork this Skill

Edit a local copy. It keeps the author and licence.

referencesrubric.md

≈448 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Rubric

Use this scorecard for chart, map, dashboard, and statistical-figure reviews. It is for diagnosis and prioritization, not aesthetic policing.

Score each dimension from 0 to 5. A 5 is excellent, 3 is usable but improvable, 1 is seriously weak, and 0 is absent or actively misleading.

Dimensions

Analytical purpose: the display answers a clear quantitative question and the title or caption frames that question honestly.

Graphical integrity: scales, baselines, transformations, encodings, denominators, uncertainty, and source support a truthful reading.

Data visibility: relevant observations, variation, outliers, distribution, or uncertainty are visible rather than hidden behind excessive aggregation.

Comparison power: ordering, alignment, scales, grouping, and labels make the intended comparison easy.

Economy of means: most marks encode data or useful structure; decoration and redundancy do not compete with evidence.

Annotation and context: labels, units, definitions, source, events, thresholds, and caveats appear close enough to the data to reduce ambiguity.

Accessibility and robustness: text is legible, color is not the only carrier of meaning, contrast is adequate, and the chart works in its intended medium.

Overall interpretation

30 to 35: strong display; focus on polish or publication constraints.

23 to 29: usable, with one or two high-impact fixes.

15 to 22: materially weak; redesign the comparison or restore missing context.

Below 15: likely misleading or ineffective; start from the analytical question and rebuild.

Prioritization

When time is short, fix issues in this order: misleading magnitude, missing denominators or uncertainty, wrong chart for the task, hidden data, weak comparison, accessibility, then cosmetic cleanup.

Source: SKILL.md on GitHub

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No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill is safe to use and adheres to best practices for data visualization auditing. It provides analytical scripts to evaluate chart integrity based on Tufte's principles. All scripts use only the Python standard library, do not perform network requests, and do not access sensitive system files. A minor risk is documented regarding the attack surface for indirect prompt injection when processing untrusted user data.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Signed by skilld at 3323bc9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Steadyupdated last month
Other metadata
compatibility
Bundled helpers require Python 3.10+ and use the standard library. The SVG renderer is a first-pass tool, not a full plotting or accessibility-validation system.
metadata
{
  "version": "2.1.0",
  "reviewed": "2026-09-13",
  "source-note": "Tufte-informed methods adapted from previously supplied study material; source book text and images are not bundled. No missing licence terms have been reconstructed."
}

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