From data-visualisation-and-publishing
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/data-visualisation-and-publishing:setup-environmentThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this after `skills/choose-tool` has selected a library. The goal is a minimal, reproducible environment the user can iterate in.
Use this after skills/choose-tool has selected a library. The goal is a minimal, reproducible environment the user can iterate in.
data/, generated output under output/ (or dist/), source under src/.project/
├── pyproject.toml # or requirements.txt
├── data/
├── src/
│ └── plots.py
├── output/
└── README.md
uv or venv to isolate.matplotlib, pandas, numpy; add bokeh or dash/plotly as needed.kaleido (Plotly) or use Bokeh's export_png (requires selenium/chromedriver or Playwright).project/
├── package.json
├── index.html
├── src/
│ └── chart.(js|ts|jsx|tsx)
└── data/
dist/ and host as static assets.flutter create, add fl_chart to pubspec.yaml, pin a version.pip install.conda/mamba (it has non-trivial geospatial C deps); document the conda env.If the target is data storytelling (narrative scrolling, animated transitions):
narrative/ folder for Markdown/MDX copy alongside the chart code so the story and viz stay together.Always leave the user with:
python src/plots.py or npm run build).Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub danielrosehill/claude-code-plugins --plugin data-visualisation-and-publishing