Scientific Visualization
技能 已验证 活跃Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
To empower researchers to create clear, accurate, and accessible figures for scientific publications, ensuring compliance with journal standards and best practices.
功能
- Orchestrates Matplotlib, Seaborn, and Plotly for figure generation
- Provides journal-specific style presets and export functions
- Includes colorblind-friendly palettes and accessibility guidelines
- Offers detailed examples for various plot types and statistical rigor
- Automates figure export in required formats (PDF, TIFF, PNG) and resolutions
使用场景
- Creating figures for journal submission (Nature, Science, Cell, etc.)
- Ensuring figures are colorblind-friendly and accessible
- Making multi-panel figures with consistent styling
- Exporting figures at correct resolution and format (PDF, EPS, TIFF)
- Improving existing figures to meet publication standards
非目标
- Direct data analysis or statistical modeling (relies on external libraries)
- Generating figures for non-scientific contexts (e.g., marketing, general presentations)
- Providing a GUI-based plotting tool (operates via code and agent prompts)
实践
- Data visualization
- Scientific communication
- Publication preparation
先决条件
- Python 3.11+ recommended
- Matplotlib, Seaborn, Plotly (installed by agent)
- uv (for dependency management)
Execution
- info:Pinned dependenciesWhile the project uses 'uv' for package management, explicit lockfiles or pinned versions for the core libraries (matplotlib, seaborn, plotly) are not directly evident in the skill's immediate files, though the overall repo may have them.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
AlterLab Scientific Viz
90Part of the AlterLab Academic Skills suite. Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Data Visualization
86Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Create Viz
75Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Render Publication Graphic
100Produce publication-ready 2D graphics with proper DPI, color profiles, typography, and export formats for print and digital media. Use when preparing figures for academic journal submission, creating graphics for print publications, ensuring graphics meet publisher technical specifications, exporting visualizations for web with proper optimization, or creating multi-format exports from a single source.
Seaborn Statistical Visualization
98Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Alterlab Seaborn
95Part of the AlterLab Academic Skills suite. Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.