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scientific-visualization

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

plugins/KalarisLabs/research-agent-skills/skills/scientific-visualization/SKILL.md

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# Scientific Visualization

Build figures that preserve scientific meaning before optimizing appearance. Separate universal principles from dated publisher rules, preserve raw data and transformations, use color redundantly, and inspect delivered files rather than trusting plotting defaults.

## Non-negotiable guardrails

- Never alter, hide, invent, or selectively enhance data to improve a figure.
- Preserve raw tables/images, exclusions, missing-value codes, analysis code, normalization, binning, image adjustments, and random seeds.
- Do not infer journal requirements. Identify the exact journal, article type, figure type, and submission phase; verify its live official guidance.
- Do not claim that a palette, DPI value, format, or automated report makes a figure accessible or journal-compliant.
- Do not silently connect missing observations, suppress inconvenient points, upsample images as if detail increased, or tune axes/dual axes to exaggerate a conclusion.
- Keep interactive and static outputs as distinct deliverables. Interactive hover is not a substitute for labels, alt text, keyboard access, an accessible data table, or a static fallback.

Read `references/publication_guidelines.md` for deceptive-encoding and integrity checks. Read `references/journal_requirements.md` only after the target and phase are known.