domains / families / science / scientific-visualization

scientific-visualization

combined

Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning. 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.

When to use

• Creating plots or visualizations for scientific manuscripts
• Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
• Ensuring figures are colorblind-friendly and accessible
• Making multi-panel figures with consistent styling
• Exporting figures at correct resolution and format
• Following specific publication guidelines
• Improving existing figures to meet publication standards
• Creating figures that need to work in both color and grayscale

When not to use

• altering, hiding, inventing, or selectively enhancing data
• inferring journal requirements without verification
• claiming automated reports make figures accessible or compliant
• silently connecting missing observations or suppressing points
• upsampling images as if detail increased
• tuning axes to exaggerate a conclusion

Skill page

Conflict

The K-Dense-AI copy is a distinct, more rigorous procedure that adds non-negotiable data-integrity guardrails, a structured evidence/destination workflow, and specific Matplotlib layout/norm constraints. The other three copies are near-identical reproductions of a simpler, style-focused guide. Crowning one of the simpler copies would hide the real alternative provided by the K-Dense-AI skill, so the copies should be combined.