Agent skill · design visual · k-dense-ai
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.
Why this skill is useful
Provides domain-specific knowledge on creating publication-ready scientific figures with specific guidelines and best practices that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, pillow, plotly, pypdf, python, seaborn installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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/publicationguidelines.md for deceptive-encoding and integrity checks. Read references/journalrequirements.md only after the target and phase are known. Workflow 1. Define the evidence and destination Record: audience and medium: manuscript, web, slide, poster, supplement; exact publisher/journal, article type, submission phase, and intended final width; variable semantics, units, sample/replicate structure, missing/censored values; estimator and uncertainty definition; transformations: filtering, aggregation, normalization, smoothing, bins, image processing; source-data paths/identifiers and output provenance. If requirements are not known, create a provisional general figure and label all publisher choices as pending verification. 2. Choose an honest encoding Prefer position on a common scale. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scientific-visualization