publication-figures
Trigger Boundary
Publication figure orchestration with contextual palette discovery, Matplotlib, Seaborn, Plotly, and export-ready layout.
Use this aggregate Codex App skill when the task matches one of the source workflows below.
Source Workflows
scientific-visualization: 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). Reference: _src/publication/source.mdmatplotlib: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Reference: _src/mpl/source.mdseaborn: 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. Reference: _src/seaborn/source.mdplotly: Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization. Reference: _src/plotly/source.md
Plugin Workflow Notes
- When no palette is specified, or when a venue/figure type/domain context is provided, first run contextual discovery with
publication-figure-palettes or ../../shared/scripts/palette.py discover.
- Keep discovery and application separate: safe canonical palettes are plotting defaults; Notion candidates are contextual, non-official, and gated for snippets.
- For figure readiness checks, use
scientific-figure-qa after plotting or layout decisions.
- Venue templates belong to research-writing; this plugin owns figure color, plotting code, visual examples, schematics, and posters.
Workflow
- Choose the source workflow whose trigger boundary best matches the user request.
- Read that source workflow's
source.md before acting.
- Load only the needed files under that workflow's copied references, scripts, assets, or evals.
- Follow the source workflow unless the current project gives stricter instructions.
1---2name: publication-figures3description: Publication figure orchestration with contextual palette discovery, Matplotlib, Seaborn, Plotly, and export-ready layout.4---56# publication-figures78## Trigger Boundary910Publication figure orchestration with contextual palette discovery, Matplotlib, Seaborn, Plotly, and export-ready layout.1112Use this aggregate Codex App skill when the task matches one of the source workflows below.1314## Source Workflows1516- `scientific-visualization`: 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). Reference: `_src/publication/source.md`17- `matplotlib`: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Reference: `_src/mpl/source.md`18- `seaborn`: 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. Reference: `_src/seaborn/source.md`19- `plotly`: Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization. Reference: `_src/plotly/source.md`2021## Plugin Workflow Notes2223- When no palette is specified, or when a venue/figure type/domain context is provided, first run contextual discovery with `publication-figure-palettes` or `../../shared/scripts/palette.py discover`.24- Keep discovery and application separate: safe canonical palettes are plotting defaults; Notion candidates are contextual, non-official, and gated for snippets.25- For figure readiness checks, use `scientific-figure-qa` after plotting or layout decisions.26- Venue templates belong to research-writing; this plugin owns figure color, plotting code, visual examples, schematics, and posters.2728## Workflow29301. Choose the source workflow whose trigger boundary best matches the user request.312. Read that source workflow's `source.md` before acting.323. Load only the needed files under that workflow's copied references, scripts, assets, or evals.334. Follow the source workflow unless the current project gives stricter instructions.
YuukiAS