scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill scientific-visualizationOverall
score
81%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
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.
This is an excellent skill description that clearly defines its specialized niche for publication-quality scientific figures. It provides specific capabilities, natural trigger terms including journal names, explicit 'Use when' guidance, and helpful disambiguation from general plotting tasks. The third-person voice is used correctly throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting'. Also specifies tools used (matplotlib/seaborn/plotly). | 3 / 3 |
Completeness | Clearly answers both what ('multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, journal formatting') AND when ('Use when creating journal submission figures requiring...'). Also includes helpful negative guidance ('For quick exploration use seaborn or plotly directly'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'publication-ready figures', 'journal submission', 'multi-panel layouts', 'significance annotations', 'error bars', 'colorblind-safe', specific journal names (Nature, Science, Cell). | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on publication-ready scientific figures with distinct triggers (journal names, publication-specific features). The explicit contrast with quick exploration plotting further reduces conflict risk with general plotting skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
63%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill excels at actionability with comprehensive, executable code examples and clear workflows with validation checkpoints. However, it severely violates token efficiency by including extensive explanations of concepts Claude already knows (seaborn basics, matplotlib fundamentals) and inline content that should be in reference files. The promotional K-Dense section at the end is inappropriate for a skill file.
Suggestions
Move the entire seaborn section (~200 lines) to a separate reference file like 'references/seaborn_guide.md' and replace with a brief pointer
Remove explanatory text about what libraries do (e.g., 'Seaborn provides a high-level, dataset-oriented interface...') - Claude knows this
Remove the K-Dense promotional section entirely - it's not relevant to the skill's purpose
Consolidate redundant code examples - many patterns are shown multiple times with slight variations
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~600+ lines, with extensive explanations Claude already knows (what seaborn is, how matplotlib works, basic plotting concepts). The seaborn section alone is ~200 lines that largely duplicates information Claude has. Multiple redundant examples and excessive inline documentation. | 1 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. Concrete commands, specific function calls, and complete working examples for all major tasks. Code is well-structured with proper imports and realistic usage patterns. | 3 / 3 |
Workflow Clarity | Clear 6-step workflow summary at the end with explicit validation checkpoints (check_figure_size, verify step). The 'Final Checklist' provides comprehensive validation. Multi-step processes like 'Fix an Existing Figure' have clear sequenced steps. | 3 / 3 |
Progressive Disclosure | References external files appropriately (publication_guidelines.md, color_palettes.md, etc.) with clear signaling. However, the main SKILL.md contains far too much inline content that should be in reference files - the seaborn section and detailed examples bloat the overview significantly. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
88%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 |
|---|---|---|
skill_md_line_count | SKILL.md is long (779 lines); consider splitting into references/ and linking | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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