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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.

68

Quality

82%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

The skill body is a well-structured, concise instruction-only skill that appropriately splits detail into real one-level-deep reference files and gives concrete procedural and output guidance. The main room for improvement is adding concrete worked examples (e.g., a chart-type-to-question decision table) to push actionability higher.

Suggestions

Add a small decision table mapping common statistical questions (shape, spread, outliers, comparison) to recommended encodings to make guidance more concretely actionable.

Include one short worked example of critiquing a misleading summary (e.g., a mean-only bar chart hiding variance) to anchor the Output Expectations.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — Overview, Default assumption, Working Pattern, and Output Expectations each earn their place with no padding or explanation of concepts Claude already knows.

5 / 5

Actionability

The Working Pattern and Output Expectations give concrete, specific procedural and output guidance rather than vague direction; it falls short of a 5 only because it lacks concrete named-example coverage (e.g., a chart-type decision mapping) for common cases.

4 / 5

Workflow Clarity

The 4-step Working Pattern is a clearly sequenced decision/design workflow with no destructive or batch operations triggering the validation cap; it is not a 5 because it contains no explicit validation checkpoints.

4 / 5

Progressive Disclosure

The body is a clear overview that points to four real, one-level-deep bundle files under a well-labeled References section split into Shared theory and Skill references, making navigation easy.

5 / 5

Total

18

/

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.

The description is strong: it clearly states what the skill does and when to use it with concrete trigger phrases and good keyword coverage. The main weakness is specificity, since it relies on a single broad action verb rather than enumerating multiple concrete capabilities.

Suggestions

Replace or expand the single verb "Design" with 2-3 concrete actions (e.g., "Choose encodings for distributions and intervals, surface missingness and sample size, and critique misleading summaries") to lift specificity.

Add a couple of natural everyday terms users might say (e.g., "error bars", "box plots", "histograms") to round out trigger-term coverage.

DimensionReasoningScore

Specificity

"Design statistically honest and uncertainty-aware visualizations" names the domain clearly but offers only a single broad action verb, fitting the anchor for naming the domain with 1-2 concrete actions but not comprehensive coverage.

3 / 5

Completeness

It explicitly answers "what" ("Design statistically honest and uncertainty-aware visualizations") and "when" ("Use when the user needs help showing...") with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

The phrase "distributions, intervals, confidence, missingness, sampling effects, or analytical rigor in charts and dashboards" provides good natural keyword coverage with multiple synonyms, though a few everyday variations are absent.

4 / 5

Distinctiveness Conflict Risk

The statistical-honesty/uncertainty niche is clearly carved out with distinct triggers, with only minor overlap risk against a general visualization skill.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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