creating-data-visualizations
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill creating-data-visualizationsGenerate 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'.
Validation
81%| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
Implementation
7%This skill content is almost entirely generic boilerplate that provides no actionable guidance for data visualization. It lacks any executable code examples, specific library recommendations (matplotlib, seaborn, plotly), or concrete workflows. The content explains concepts Claude already understands while failing to provide the specific, copy-paste-ready code that would make this skill useful.
Suggestions
Replace abstract descriptions with executable Python code examples using specific libraries (e.g., matplotlib, seaborn, plotly) showing actual visualization generation
Remove generic sections like 'How It Works', 'When to Use', 'Integration', 'Error Handling' that explain concepts Claude already knows
Add concrete code templates for common visualization types (bar charts, line plots, scatter plots, histograms) with real data examples
Include specific guidance on visualization selection criteria (e.g., 'Use scatter plots for two continuous variables, bar charts for categorical comparisons')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with unnecessary explanations of concepts Claude already knows (what data visualization is, how analysis works). Sections like 'How It Works', 'When to Use', and 'Integration' add no actionable value and pad the content significantly. | 1 / 3 |
Actionability | No executable code, no specific libraries mentioned, no concrete commands. Examples describe what 'the skill will do' abstractly rather than providing actual Python/matplotlib/seaborn code that Claude could execute. | 1 / 3 |
Workflow Clarity | The 'Instructions' section is completely generic boilerplate ('Invoke this skill when trigger conditions are met'). No actual workflow for creating visualizations, no validation steps, no specific sequence of operations. | 1 / 3 |
Progressive Disclosure | Content is organized into sections with headers, but it's a monolithic file with no references to external resources. The structure exists but contains mostly filler content rather than appropriately split detailed materials. | 2 / 3 |
Total | 5 / 12 Passed |
Activation
82%This is a reasonably well-constructed description with strong trigger term coverage and complete what/when guidance. The main weaknesses are the somewhat generic action verbs that could cause conflicts with other skills, and the lack of specific visualization types or data format details that would make the capabilities more concrete.
Suggestions
Add specific visualization types (e.g., 'bar charts, line graphs, scatter plots, histograms') to improve specificity
Remove or qualify generic trigger terms like 'generate', 'create', 'scaffold' which could conflict with many other skills - consider 'generate visualization' or 'create chart' as compound triggers instead
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (data visualization) and lists general actions (plots, charts, graphs, automatic type selection), but doesn't specify concrete capabilities like 'create bar charts, scatter plots, histograms' or mention specific data formats. | 2 / 3 |
Completeness | Clearly answers both what (generate plots, charts, graphs with automatic type selection) and when (explicit 'Use when' clause with trigger terms and phrases). The description provides explicit guidance on when Claude should select this skill. | 3 / 3 |
Trigger Term Quality | Includes good coverage of natural terms users would say: 'visualization', 'plot', 'chart', 'graph', 'generate', 'create', 'scaffold'. These are common words users naturally use when requesting data visualizations. | 3 / 3 |
Distinctiveness Conflict Risk | While 'visualization', 'plot', 'chart', 'graph' are fairly specific, terms like 'generate', 'create', 'scaffold' are very generic and could conflict with many other skills (code generation, document creation, etc.). | 2 / 3 |
Total | 10 / 12 Passed |
Reviewed
Table of Contents
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