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Scientific Figure Visualization

Design, audit and export honest, accessible publication-ready scientific figures with Matplotlib, Seaborn or Plotly.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

Once installed, the agent plans scientific charts so they show data truthfully: it checks axis choices, uncertainty labels, missing-data display and color contrast, and warns against misleading encodings. It can write plotting code with bundled style presets and colour palettes, check image metadata and palette contrast, and produce an export plan with figure sizes for selected journal profiles. These profiles are dated planning snapshots, not verified journal requirements, so the agent is instructed to confirm the live guidance itself.

Great for

  • Researchers preparing figures for a manuscript, poster or slides who want careful, honest layout
  • Data analysts making charts with Matplotlib or Seaborn who want accessibility and uncertainty checks
  • Students learning how to present scientific data without distorting it

Skip it if

  • Final journal submission checks: the skill explicitly does not certify compliance, so the target journal's current instructions must be verified by a person
  • Situations needing automatic image editing of raw experimental data, since the skill forbids altering or enhancing data to improve appearance

Try asking

  • “Make a multi-panel Matplotlib figure of treatment response over time with 95% confidence intervals and a colour-blind-safe palette”
  • “Audit this chart's colour palette and contrast for accessibility and tell me what redundant encodings to add”
  • “Create an export plan for a 89 mm single-column figure for a journal, with a checklist of items to verify”
  • “Check the metadata of this TIFF image and warn me about any whole-image adjustments before I publish”
Step-by-step guide: how to use Scientific Figure VisualizationInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    scientific-visualization.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip scientific-visualization.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scientific-visualization.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Tell the agent the exact journal, article type and submission phase up front, and have it mark publisher rules as pending verification until you confirm them on the journal's official site.
  • Ask for raw data paths and the uncertainty definition (SD, SE or CI, with n) so the figure labels are accurate.
  • Review the scripts before running them, and run them on copies of your files, since they read and write local image and data files.

What's inside

  • assets/color_palettes.py
  • assets/publisher_profiles.json
  • references/color_palettes.md
  • references/journal_requirements.md
  • references/matplotlib_examples.md
  • references/publication_guidelines.md
  • references/sources.md
  • scripts/_common.py
  • scripts/export_plan.py
  • scripts/figure_export.py
  • scripts/image_metadata.py
  • scripts/palette_audit.py
  • scripts/style_presets.py
  • scripts/style_preview.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.