setup-environment

name	setup-environment
description	Stand up a working data visualisation environment in a repo or workspace — install deps, create a starter project, wire up a reproducible build. Use after a tool has been chosen and the user needs it ready to run.

Set Up a Data Visualisation Environment

Use this after skills/choose-tool has selected a library. The goal is a minimal, reproducible environment the user can iterate in.

General principles

• Reproducible by default: pin versions, commit lockfiles.
• One tool per project unless the user has asked for a composite.
• Separate data from code: raw input under data/, generated output under output/ (or dist/), source under src/.
• Headless-capable: if the target is static figures in a report, ensure the build can run without a display server.

Recipe: Python (Matplotlib / Bokeh / Plotly Dash)

project/
├── pyproject.toml        # or requirements.txt
├── data/
├── src/
│   └── plots.py
├── output/
└── README.md

• Use uv or venv to isolate.
• Pin: matplotlib, pandas, numpy; add bokeh or dash/plotly as needed.
• For static export from Plotly/Bokeh, install kaleido (Plotly) or use Bokeh's export_png (requires selenium/chromedriver or Playwright).

Recipe: Web publishing (Chart.js / Chartist / ECharts / D3 / visx / Vizzu / VChart / Lightweight Charts / react-globe.gl)

project/
├── package.json
├── index.html
├── src/
│   └── chart.(js|ts|jsx|tsx)
└── data/

• Default to Vite for vanilla JS/TS or React.
• For static deploys (report embeds), build to dist/ and host as static assets.
• For D3 / visx, prefer TypeScript for maintainability on anything non-trivial.

Recipe: Flutter (fl_chart)

• Scaffold with flutter create, add fl_chart to pubspec.yaml, pin a version.

Recipe: Scientific / desktop (Constellation, DataWarrior, Iris)

• Constellation and DataWarrior are desktop apps — download the release binary, don't attempt to pip install.
• Iris is a Python library — install with conda/mamba (it has non-trivial geospatial C deps); document the conda env.

Data storytelling extras

If the target is data storytelling (narrative scrolling, animated transitions):

• Scaffold in web stack (Vizzu or VChart).
• Add a narrative/ folder for Markdown/MDX copy alongside the chart code so the story and viz stay together.

Deliverables

Always leave the user with:

1. A working "hello chart" example that renders against sample data.
2. A documented command to rebuild (e.g. python src/plots.py or npm run build).
3. A one-line note on how to export to the intended publishing target (PNG, SVG, HTML embed, PDF figure).