Data visualisation

Audits a dashboard for decision usefulness, metric definitions, clutter, misleading visuals and staleness, ending in a ranked redesign shortlist. Use when a dashboard is ignored or distrusted.

Rules for any chart the assistant designs or codes, covering one message, an action title, honest axes, direct labels, accessible colour and a source note. Load whenever a chart or plot is made.

Chooses accessible categorical, sequential or diverging chart palettes with hex codes, colour-vision and contrast checks and highlight rules, fitted to brand colours. Use when colouring charts.

Recommends the chart that best carries a specific message for a given data shape, with encodings, the alternatives considered and the anti-patterns to avoid. Use before building a chart.

Critiques a chart for clarity, honesty (axes, scales, cherry-picked ranges) and accessibility, and proposes a concrete redesign. Use before a chart goes into a deck, report or dashboard.

Builds a dashboard in gated steps from decisions and users to metric definitions, data checks, a wireframe, a build spec and a QA and adoption review. Use when a dashboard must be trusted and used.

Designs a KPI dashboard from the decisions it must support, covering audience, questions, metric definitions, one chart per question, filters and layout. Use before building it in a BI tool.

Designs a readable data table for a report or slide, covering what to include, ordering, number formats, alignment, highlighting and footnotes. Use when your tables get skipped or misread.

Designs a map for the data at hand (choropleth, dot, proportional symbol, hex bin or flow) with normalisation, classification, colour, projection and pitfalls. Use before putting data on a map.

Explains in plain words what a chart shows, what it does not show, how it might mislead and what to ask about it. Use when you are handed a chart in the news, a report or a meeting.

Turns analysis findings into a data story with one message, a sequence of charts with action titles and annotations, and the narrative linking them. Use when presenting to non-analysts.

Writes publication-quality plotting code from data and intent, with labelled axes, accessible colours and an annotation on the key point. Use for matplotlib, seaborn, plotly, ggplot2 or Vega-Lite.