visualization-best-practices
Visualization Best Practices - Auto-activating skill for Data Analytics. Triggers on: visualization best practices, visualization best practices Part of the Data Analytics skill category.
32
0%
Does it follow best practices?
Impact
92%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/12-data-analytics/visualization-best-practices/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is critically underdeveloped. It provides no concrete actions, no meaningful trigger terms beyond a single repeated phrase, no 'Use when...' guidance, and no distinguishing details. It would be nearly impossible for Claude to reliably select this skill from a pool of similar data analytics skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Recommends chart types, applies color accessibility standards, optimizes axis labels and legends, and suggests layout improvements for data visualizations.'
Add an explicit 'Use when...' clause with diverse trigger scenarios, e.g., 'Use when the user asks about chart design, graph formatting, choosing chart types, improving data visualizations, color palettes for plots, or making dashboards more readable.'
Expand trigger terms to include natural user language variations such as 'chart', 'graph', 'plot', 'dashboard', 'data viz', 'color scheme', 'axis labels', 'legend placement', and specific chart types like 'bar chart', 'scatter plot', 'heatmap'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. It only names a domain ('visualization best practices') without describing what the skill actually does—no verbs like 'creates', 'recommends', 'applies', or any specific capabilities. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming a topic, and the 'when' guidance is limited to a redundant trigger phrase with no explicit 'Use when...' clause or meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'visualization best practices' repeated twice. This misses natural user language like 'chart design', 'graph formatting', 'plot styling', 'data viz', 'color palette', or specific chart types users might mention. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic within the data analytics domain. 'Visualization best practices' could overlap with any charting, plotting, dashboard, or data presentation skill, and provides no distinguishing specifics. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an empty shell with no substantive content. It consists entirely of auto-generated boilerplate that describes what the skill would do without actually providing any visualization best practices, concrete guidance, code examples, or actionable instructions. It fails on every dimension of the rubric.
Suggestions
Replace the meta-description sections with actual visualization best practices content: chart type selection guidelines, color accessibility rules, labeling standards, and common anti-patterns to avoid.
Add concrete, executable code examples (e.g., Python matplotlib/seaborn snippets) demonstrating recommended visualization patterns with before/after comparisons.
Include a decision workflow: e.g., 'If comparing categories → use bar chart; if showing trends over time → use line chart; if showing distribution → use histogram' with validation steps for checking readability and accessibility.
Remove all self-referential boilerplate ('This skill activates when...', 'Example Triggers') and replace with actionable content that teaches visualization best practices directly.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual visualization best practices, concrete guidance, or useful information. Every section restates the same vague idea. | 1 / 3 |
Actionability | There is zero actionable content—no concrete code, no specific visualization guidelines, no examples of chart types, color palettes, accessibility tips, or any executable guidance. It only describes itself rather than instructing. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or sequences of any kind. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive set of sections with no references to detailed materials, no links to examples or advanced guides, and no meaningful structure beyond boilerplate headings. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 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 | 9 / 11 Passed | |
- Commit
4dee593
- Reviewed
Table of Contents
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