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statistical-and-uncertainty-visualization

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.

64

Quality

80%

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SecuritybySnyk

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Fix and improve this skill with Tessl

tessl review fix ./plugins/build-web-data-visualization/skills/statistical-and-uncertainty-visualization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A token-efficient, well-organized conceptual skill body that correctly delegates detail to reference files and provides a clear working pattern. Its weaknesses are abstract guidance that never names a concrete encoding choice, and two shared-theory reference paths that resolve to nonexistent files.

Suggestions

Inline one or two concrete encoding pairings (e.g., 'quantile dot plot or interval band for estimates; annotate n per group for sample size') so the body is actionable without opening references.

Fix or remove the two broken shared-theory paths under `../../references/foundations/` — they do not resolve to real files and break navigation.

Add a lightweight decision checkpoint to the Working Pattern, such as confirming the chosen encoding preserves the variation relevant to the viewer's decision before finalizing the chart.

DimensionReasoningScore

Conciseness

The ~40-line body is lean with no padding and no explanation of concepts Claude already knows; sections like "Default assumption: if a claim depends on variability... the visualization should show that explicitly" each earn their place. It matches the 'lean and efficient; every token earns its place' anchor.

5 / 5

Actionability

Steps like "Choose encodings that show spread, uncertainty, missingness, or sample size honestly" and "Name the statistical question, not just the chart type" are concrete behaviors but name no specific encodings, chart choices, or worked examples — the executable specifics are deferred to references. This sits between 'minimal concrete guidance' (2) and 'mostly executable guidance' (4).

3 / 5

Workflow Clarity

The 4-step "Working Pattern" is a coherent, ordered design sequence and the task is non-destructive, so validation checkpoints are largely not applicable. It matches 'clear sequence with most checkpoints present; minor gaps' — an inline decision checkpoint (e.g., 'verify the chosen encoding still shows the variation that matters before finalizing') would push it to 5.

4 / 5

Progressive Disclosure

The body is a concise overview with a clearly-labeled References section and one-level-deep skill references (`./references/*.md`, all four of which exist). However, both shared-theory paths (`../../references/foundations/task-abstraction-and-chart-selection.md`, `../../references/foundations/perception-color-and-encoding.md`) do not exist on disk, so navigation to a third of the referenced material is broken — more than the 'minor organization gaps' of the 4 anchor.

3 / 5

Total

15

/

20

Passed

Description

78%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A well-formed description with an explicit 'Use when...' clause, third-person voice, and strong natural trigger terms. Its main weaknesses are a single-action 'what' statement that could enumerate more concrete capabilities, and a slightly broad 'charts and dashboards' trigger that invites overlap with general visualization skills.

DimensionReasoningScore

Specificity

"Design statistically honest and uncertainty-aware visualizations" names the domain clearly but offers only one concrete action (designing visualizations) qualified by adjectives, matching the 'names domain and 1-2 concrete actions' anchor. It does not list several specific actions (e.g., choosing interval encodings, annotating missingness) that would warrant a 4.

3 / 5

Completeness

It explicitly answers what ("Design statistically honest and uncertainty-aware visualizations") and when ("Use when the user needs help showing distributions, intervals, confidence, missingness, sampling effects, or analytical rigor") with concrete trigger phrases, matching the top anchor. Third-person voice is used correctly.

5 / 5

Trigger Term Quality

Natural trigger terms like "distributions, intervals, confidence, missingness, sampling effects" plus "charts and dashboards" cover the phrases users would plausibly say (e.g., 'how do I show confidence intervals'). A few common synonyms are missing (e.g., 'error bars', 'spread', 'variance'), so it falls just short of the comprehensive 5 anchor.

4 / 5

Distinctiveness Conflict Risk

The statistical-honesty niche (missingness, sampling effects, confidence) is fairly distinct from general chart-styling skills, but the trailing trigger "charts and dashboards" is broad enough to overlap with general dataviz skills. This is 'mostly distinct; minor overlap risk with closely related skills' rather than the minimal-conflict 5.

4 / 5

Total

16

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

openai/plugins

Path

plugins/build-web-data-visualization/skills/statistical-and-uncertainty-visualization/SKILL.md

Reviewed

Table of Contents

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