statistical-and-uncertainty-visualization — Codex skill: honest charts
Builds charts and dashboards that show spread, intervals and missing data instead of hiding them behind a single summary line.
npx skills add openai/plugins --skill statistical-and-uncertainty-visualization -a codexWhat statistical-and-uncertainty-visualization does
Reach for this skill when the risk is analytical distortion, not rendering difficulty. It covers distributions, intervals, uncertainty, missingness, aggregation effects and the usual ways a chart misleads. The default assumption is blunt: if a claim depends on variability, estimation, sampling or model uncertainty, the visualization should show it.
The working pattern runs in four steps. First, figure out whether the viewer needs exact values, distributions, intervals or model-derived estimates. Then pick encodings that show spread, uncertainty, missingness and sample size honestly. Next, check that aggregation or smoothing has not summarized away the variation the decision depends on. Short explanations go next to the view when the uncertainty concept is not obvious. Reference files cover summary choices, uncertainty encodings, analytical pitfalls and missingness.
What you get back is the statistical question named in words, a reason the chosen encoding is more truthful than the tempting alternative, and a flag wherever aggregation or interval choice can mislead. The skill ships in the openai/plugins repository under the MIT license.
What it helps with
- show spread instead of one average line
- pick between histogram, box plot and density plot
- draw confidence intervals without misleading readers
- surface missing data and sample size on a dashboard
- spot where aggregation eats the variation that matters
Example prompts for the agent
- What chart should I use to show uncertainty in this data?
- Should this be a histogram, box plot, violin plot, or density plot?
- This dashboard hides variability. How should I fix it?
How to install statistical-and-uncertainty-visualization
1Skills CLI (Vercel)
npx skills add openai/plugins --skill statistical-and-uncertainty-visualization -a codex2Manually: put the files in place
.agents/skills/statistical-and-uncertainty-visualization/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add openai/plugins --skill statistical-and-uncertainty-visualization -a claude-code2Manually: put the files in place
~/.claude/skills/statistical-and-uncertainty-visualization/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/statistical-and-uncertainty-visualization/
Manually: put the files in place
.cursor/skills/statistical-and-uncertainty-visualization/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install openai/plugins statistical-and-uncertainty-visualization2Manually: put the files in place
.github/skills/statistical-and-uncertainty-visualization/SKILL.mdPlatform CLI
gemini skills install https://github.com/openai/plugins --path plugins/build-web-data-visualization/skills/statistical-and-uncertainty-visualizationManually: put the files in place
.devin/skills/statistical-and-uncertainty-visualization/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them
Questions about statistical-and-uncertainty-visualization
How do I install statistical-and-uncertainty-visualization in Claude?
Run npx skills add openai/plugins --skill statistical-and-uncertainty-visualization, then npx skills add openai/plugins --skill statistical-and-uncertainty-visualization -a codex. The skill file then sits at .agents/skills/statistical-and-uncertainty-visualization/SKILL.md.
What does statistical-and-uncertainty-visualization do?
It helps design charts and dashboards that show distributions, intervals, confidence, missingness and sampling effects honestly. The skill names the statistical question and explains why the chosen encoding is more truthful.
How is it different from generic chart advice?
It targets cases where the main risk is analytical distortion rather than rendering difficulty. Aggregation, smoothing and interval choice get called out as places where a chart can mislead.
Source text
SKILL.md · MIT
--- name: statistical-and-uncertainty-visualization description: Design statistically honest and uncertainty-aware visualizations. Use when the user needs help showing distributions, intervals, confidence, missingness, sampling effects, or analytical rigor in charts and dashboards. --- # Statistical and Uncertainty Visualization ## Overview Use this skill when the risk is analytical distortion rather than rendering difficulty. This skill focuses on distributions, intervals, uncertainty, missingness, aggregation effects, and common statistical storytelling failures. Default assumption: if a claim depends on variability, estimation, sampling, or model uncertainty, the visualization should show that explicitly. ## Working Pattern 1. Identify whether the viewer needs exact values, distributions, intervals, or model-derived estimates. 2. Choose encodings that show spread, uncertainty, missingness, or sample size honestly. 3. Avoid summarizing away the variation that matters to the decision. 4. Pair concise explanations with the view when the uncertainty concept is nontrivial. ## Output Expectations - Name the statistical question, not just the chart type. - Explain why the chosen encoding is more truthful than the tempting alternative. - Call out when aggregation, smoothing, or interval choice can mislead. ## References - Shared theory: - `../../references/foundations/task-abstraction-and-chart-selection.md` - `../../references/foundations/perception-color-and-encoding.md` - Skill references: - `./references/distribution-and-summary-choices.md` - `./references/uncertainty-encodings.md` - `./references/experimental-and-analytical-pitfalls.md` - `./references/missingness-and-confidence.md` ## Representative Prompts - "What chart should I use to show uncertainty here?" - "Should this be a histogram, box plot, violin plot, or density plot?" - "How do I show confidence intervals without misleading people?" - "This dashboard hides variability. How should I fix it?" - "Help me visualize missing data and sample size honestly."
© OpenAI · MIT · openai/plugins