data-visualisation-and-publishing

Data Visualisation And Publishing

Create static and interactive data visualisations for reports, repos, and data storytelling — prioritising a curated inventory of open-source tools.

This plugin opinionates the tool choice. Instead of reaching for whatever library the model happens to recall, it selects from a maintained inventory (Matplotlib, Bokeh, Chart.js, ECharts, D3, visx, Vizzu, VChart, Plotly Dash, Lightweight Charts, fl_chart, Constellation, DataWarrior, Iris, QuantInvestStrats, react-globe.gl, and others) and matches the tool to presentation purpose, data shape, runtime, and audience.

Skills

• tool-inventory — canonical catalogue of preferred libraries grouped by use case (scientific, web, app, animated, Python, geospatial).
• choose-tool — decision protocol that maps presentation purpose + data shape + runtime to a tool in the inventory.
• setup-environment — scaffolds a reproducible dataviz project (Python / web / Flutter / desktop) with a working "hello chart" example.
• data-storytelling — narrative-driven visualisation design: headline-first charts, annotation, animated transitions, interactive elements that earn their place.

Commands

• /dataviz-pick — pick the right tool from the inventory for a given task.
• /dataviz-setup — scaffold a dataviz environment using the chosen tool.
• /dataviz-story — design and build a data-storytelling piece.

Philosophy

1. Pick from the inventory first. Only reach outside if nothing fits, and justify it.
2. Match the medium. Static PDF figure, web embed, dashboard, animated story, and geospatial all want different tools.
3. Storytelling ≠ dashboards. If the goal is persuasion, build one chart one claim; if the goal is exploration, build a dashboard.
4. Reproducible by default. Pin deps, commit lockfiles, keep data/code/output separate.

Installation

claude plugins install data-visualisation-and-publishing@danielrosehill

License

MIT