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Scientific Visualization (publication-ready figures)

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

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Enforces honest encodings: zero baselines for bars, disclosed axis breaks, log-axis handling of zeros, area-not-radius scaling, and explicit treatment of missing, censored, and excluded values.
  • Bakes in accessibility: redundant cues beyond color, Okabe-Ito and Paul Tol palettes, WCAG 2.2 contrast targets (4.5:1 text, 3:1 graphical objects), alt text and data alternatives.
  • Guides implementation: object-oriented Matplotlib with scoped style_context, layout="constrained" pitfalls, Seaborn 0.13 errorbar API, and Kaleido v1 rules for Plotly static export.
  • Exports with provenance: figure_export writes atomically, refuses silent overwrite, keeps vector DPI for embedded rasters, and records raw data, transformations, uncertainty, and seeds in a manifest.
  • Ships auditing CLIs: image/PDF/SVG metadata inspection, palette contrast and grayscale screening, and dated publisher export-planning profiles.

Who it's for

  • Researchers and grad students preparing figures for journals, theses, posters, or talks
  • Anyone who has been bounced by a publisher over size, DPI, format, or font embedding
  • Data analysts who need figures that survive grayscale printing and color-vision deficiency
  • Reviewers and lab managers checking figure integrity before submission

Example uses

  1. "Make a single-column combination figure for Nature" → 89 mm width, constrained layout, Okabe-Ito palette, then export_plan.py --publisher nature --figure-type combination --width single to screen it.
  2. "Check whether figure1.tiff meets submission specs" → image_metadata.py reports effective DPI at target width, color mode, alpha policy, ICC profile, and compression.
  3. "Plot the time series with a 95% bootstrap CI and don't bridge missing points" → Seaborn errorbar=("ci", 95) with a fixed seed and gaps left explicit.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/scientific-visualization/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/scientific-visualization folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/scientific-visualization/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/scientific-visualization ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal (macOS Terminal, or WSL/Git Bash on Windows).
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/scientific-visualization ~/.claude/skills/
  5. Install Python 3.11+ and uv: curl -LsSf https://astral.sh/uv/install.sh | sh
  6. If you plan to export static Plotly images, install Chrome or Chromium (Kaleido v1 no longer bundles it).
  7. Restart Claude Code and ask something like "help me build a publication-ready figure".
  8. Smoke test: uv run --isolated --no-project --python 3.13 python ~/.claude/skills/scientific-visualization/scripts/style_presets.py --list
View source on GitHub ↗License: MIT