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seaborn

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

51

Quality

59%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./skills/seaborn/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

56%Scale 1-5

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

This skill is a comprehensive seaborn reference with strong, executable code examples covering all major plot types and customization options. However, it is significantly over-verbose for a skill file — it reads like library documentation rather than a concise skill that assumes Claude's existing knowledge. Much of the content (design philosophy, data structure explanations, basic concept descriptions) should either be removed or moved to reference files.

Suggestions

Reduce the main SKILL.md to ~100-150 lines by moving detailed parameter listings, color palette references, and plot type catalogs into the referenced files (function_reference.md, objects_interface.md). Keep only quick-start, key decision points (axes vs figure-level), and the most common patterns inline.

Remove sections that explain concepts Claude already knows: 'Design Philosophy', 'Data Structure Requirements' (Claude understands tidy data), and explanatory text like 'PDF files store text and images' equivalents such as 'Seaborn provides carefully designed color palettes for different data types'.

Convert the 'Best Practices' section into a concise checklist or decision table rather than verbose explanations with 'Good/Avoid' patterns that Claude can infer.

Ensure the referenced bundle files (function_reference.md, objects_interface.md, examples.md) actually exist and contain the detailed content currently bloating the main skill file.

DimensionReasoningScore

Conciseness

The skill is significantly verbose at ~500+ lines. It extensively explains concepts Claude already knows well (what long-form vs wide-form data is, what each plot type does, design philosophy, when to use axes-level vs figure-level functions). Sections like 'Design Philosophy', 'Data Structure Requirements', and much of the 'Best Practices' section explain fundamentals that Claude inherently understands. The content reads more like a tutorial/documentation page than a concise skill reference.

2 / 5

Actionability

The skill provides numerous executable, copy-paste ready code examples covering all major plot types, customization, and common patterns. The examples are concrete and use real parameter values. Minor gap: some examples reference undefined variables (e.g., `timeseries`, `data`) without setup, but this is a minor issue given the context.

4 / 5

Workflow Clarity

For a visualization library skill, there aren't destructive/batch operations requiring validation checkpoints. The skill clearly sequences common workflows (EDA, publication figures, multi-panel figures) with concrete steps. The troubleshooting section provides good error-recovery guidance. Minor gap: no explicit workflow for iterating on figure quality or checking output before saving.

4 / 5

Progressive Disclosure

The skill references three files in a `references/` directory (function_reference.md, objects_interface.md, examples.md) which is good structure, but no bundle files were provided to verify these exist. The main SKILL.md itself is monolithic — much of the detailed content (full parameter listings for every plot category, color palette details, data structure explanations) should be in reference files rather than inlined. The references section feels like an afterthought rather than an integral part of the content organization.

3 / 5

Total

13

/

20

Passed

Description

62%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 adequately identifies the domain (Seaborn) and provides a reasonable 'use when' clause, but it leans toward feature-category language rather than concrete actions or natural user trigger terms. It would benefit from listing specific plot types and common synonyms/abbreviations users would naturally use when requesting Seaborn visualizations.

Suggestions

Add specific plot types as trigger terms (e.g., 'heatmaps, scatter plots, box plots, pair plots, violin plots') to improve both specificity and trigger term quality.

Include common abbreviations and natural phrases users would say, such as 'sns', 'seaborn chart', 'distribution plot', or 'correlation matrix visualization'.

Rephrase the 'Use this skill for...' clause to be more scenario-driven, e.g., 'Use when the user asks to create statistical visualizations, plot distributions, or mentions seaborn/sns.'

DimensionReasoningScore

Specificity

Names the domain (Seaborn/Python visualization) and lists some actions like 'dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures,' but these are more like feature categories than concrete actions (e.g., no mention of specific plot types like heatmaps, scatter plots, box plots).

3 / 5

Completeness

Has a clear 'what' (Python visualization library for statistical graphics) and a 'when' via 'Use this skill for dataset-oriented plotting, multivariate analysis...' but the 'when' clause describes capabilities rather than explicit user-facing trigger scenarios (e.g., 'when the user asks to create charts with seaborn or sns').

4 / 5

Trigger Term Quality

Includes 'Seaborn', 'Python', 'visualization', 'statistical graphics', and 'plotting' which are relevant, but misses common natural terms users would say like 'chart', 'plot', 'heatmap', 'scatter plot', 'box plot', 'bar chart', 'sns', or 'seaborn import'.

3 / 5

Distinctiveness Conflict Risk

Naming 'Seaborn' specifically helps distinguish it from generic plotting skills or matplotlib skills, but terms like 'Python visualization' and 'statistical graphics' could overlap with matplotlib, plotly, or other visualization library skills.

4 / 5

Total

14

/

20

Passed

Validation

72%

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

Validation8 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (673 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

8

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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