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.
npx skills add K-Dense-AI/scientific-agent-skills --skill scientific-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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_guidelin
- Non-negotiable guardrails
- Workflow
- 1. Define the evidence and destination
- 2. Choose an honest encoding
- 3. Design accessibility in, not after
- 4. Implement with scoped styles
- 5. Export explicitly and record provenance
- 6. Inspect, compare, and review
- Pinned snapshot
- Bundled CLIs
- Inspect raster/vector metadata
- Audit palette contrast and grayscale
- Plan/screen publisher export
- Preview styles
uv run --isolated --no-project --python 3.13 \ python your_figure.py python scripts/image_metadata.py figure.tiff \ python scripts/palette_audit.py \ python scripts/export_plan.py \ python scripts/style_preview.py \ python scripts/style_presets.py --list python scripts/style_presets.py --show nature python scripts/figure_export.py --demo outputs/export-smoke --manifest
What does the scientific-visualization skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill scientific-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.