creating-data-visualizations

Generate plots, charts, and graphs from data with automatic visualization type selection. Use when requesting "visualization", "plot", "chart", or "graph". Trigger with phrases like 'generate', 'create', or 'scaffold'.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

20%

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

The body is a generic, abstract overview that restates the description at length without providing concrete code, library specifics, or validation steps, and it fails to route to the bundled scripts and assets that exist. It reads more like marketing copy than actionable guidance.

Suggestions

Replace the abstract Overview/How It Works prose with a concise quick-start showing executable code, naming concrete libraries (matplotlib/seaborn/plotly) and how to invoke scripts/data_analyzer.py.

Add explicit validation/verification checkpoints in the workflow — e.g. validate input data format before generating, and render-check the output — instead of generic steps like 'Review the generated output'.

Link to the bundle files from the body (e.g. 'See scripts/data_analyzer.py for data analysis, assets/chart_templates/ for templates') so progressive disclosure actually routes to the bundled materials rather than restating them inline.

Dimension	Reasoning	Score
Conciseness	The body rephrases the description repeatedly across Overview, How It Works, and When to Use sections with filler like 'This skill empowers Claude to transform raw data into compelling visual representations', padding the context without adding anything Claude does not already know.	1 / 3
Actionability	No concrete code, commands, or library names appear in the body; phrases like 'Claude generates the visualization using appropriate libraries' and 'Provide necessary context and parameters' are abstract descriptions rather than executable instruction.	1 / 3
Workflow Clarity	A numbered sequence exists (Analyze, Select, Generate) but there are no validation checkpoints or feedback loops for data transformation, matching the anchor of steps present but checkpoints missing or implicit.	2 / 3
Progressive Disclosure	The body has section structure but never signals or links to the bundled resources in references/, scripts/, or assets/ (e.g. data_analyzer.py), leaving content that should be split remaining inline with no navigation to the bundle.	2 / 3
	Total	6 / 12 Passed

Description

100%

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 well-crafted: it states concrete capabilities, includes natural trigger terms, and explicitly covers both what the skill does and when to use it. It is concise and distinct from other skills.

Dimension	Reasoning	Score
Specificity	It lists multiple concrete actions — 'Generate plots, charts, and graphs from data with automatic visualization type selection' — naming the domain and the specific action set rather than vague abstractions.	3 / 3
Completeness	It explicitly answers both what (generate plots/charts/graphs with automatic type selection) and when (an explicit 'Use when requesting...' trigger clause), satisfying the completeness criterion rather than capping at 2.	3 / 3
Trigger Term Quality	It provides good coverage of natural terms via 'Use when requesting "visualization", "plot", "chart", or "graph"' and 'Trigger with phrases like "generate", "create", or "scaffold"', all phrasings users would naturally say.	3 / 3
Distinctiveness Conflict Risk	The niche (data visualization) and its trigger terms (visualization, plot, chart, graph) are distinct and unlikely to fire for unrelated skills, matching the clear-niche anchor.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 2cafb23

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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