seaborn

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

Quality

70%

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Quality

Content

65%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 well-structured reference skill with executable examples and excellent progressive disclosure via real bundle files. The main weaknesses are tutorial-level over-explanation of concepts Claude already knows and the absence of a sequenced procedural workflow with validation checkpoints.

Suggestions

Trim or remove the 'Design Philosophy' principles list and the 'Advantages' bullet lists under long-form data — these restate seaborn knowledge Claude already has.

Condense the long-form vs wide-form section to a brief structural contrast plus the melt() conversion, dropping the tutorial framing.

If a representative end-to-end workflow is intended (load → shape → plot → save), sequence it explicitly with a validation checkpoint (e.g., assert dtypes/columns before plotting) to lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	Mostly efficient but several sections restate concepts Claude already knows (the 'Design Philosophy' principles list, advantages of long-form data, and a tutorial-level long-vs-wide-form explanation), so it could be tightened toward anchor-4.	3 / 5
Actionability	Provides numerous executable, copy-paste-ready code blocks (scatterplot, relplot faceting, errorbar/estimator, savefig) covering common cases with only minor gaps, matching anchor-4.	4 / 5
Workflow Clarity	Content is organized topically (environment → interfaces → API notes → best practices) but there is no multi-step procedural workflow with validation checkpoints; this is a reference/exploration skill, not a destructive or batch operation, so it lands at anchor-3 rather than lower.	3 / 5
Progressive Disclosure	The body is a clear overview pointing to seven well-signaled, one-level-deep reference files (all present and described in two locations), with content appropriately split and easy to navigate, matching anchor-5.	5 / 5
	Total	15 / 20 Passed

Description

75%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 solid, third-person description that names concrete capabilities and plot types, gives clear use guidance, and disambiguates from sibling skills. It stops just short of the top anchor on specificity, trigger terms, and explicitness of the 'when' clause.

Dimension	Reasoning	Score
Specificity	Lists concrete actions ('quick exploration of distributions, relationships, and categorical comparisons') and named plot types ('box plots, violin plots, pair plots, heatmaps'), giving multiple specific actions with only minor coverage gaps — closer to the anchor-5 example than anchor-3, but not fully comprehensive.	4 / 5
Completeness	Explicitly answers 'what' (statistical visualization with pandas integration, specific plots) and 'when' ('Use for quick exploration of...'), but the 'when' could be more pointed with concrete trigger phrases, matching the anchor-4 example rather than the fully explicit anchor-5.	4 / 5
Trigger Term Quality	Natural terms like 'distributions', 'relationships', 'categorical comparisons', and specific plot names match what users say, with good keyword coverage; a few common synonyms ('correlation', 'scatter', 'trend') are missing, so it sits at 4 rather than 5.	4 / 5
Distinctiveness Conflict Risk	The seaborn-specific niche plus explicit boundary routing ('For interactive plots use plotly; for publication styling use scientific-visualization') make it mostly distinct with only minor overlap risk against adjacent visualization skills.	4 / 5
	Total	16 / 20 Passed

Validation

100%

Warnings & errors only

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: K-Dense-AI/scientific-agent-skills
Path: skills/seaborn/SKILL.md
Commit: b2a92ba

Reviewed: 3 days ago

Table of Contents

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