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data-storytelling

Skill2 starsChanged 12 days agoMITView on GitHub

Data Storytelling. Turn analysis into a decision-first narrative and interactive what-if analytics - SCQA story structure, findings-first headings, chart design, ipywidgets dashboards (threshold explorer, prevalence explorer, what-if scenario analyzer, cost-matrix editor, cohort filter, scenario comparison, sensitivity view), static fallback, nbconvert HTML and Voila export. Use for reports, presentations of results, executive summaries, dashboards in notebooks, interactive widgets or explaining findings to stakeholders.

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  • AnthropicSkill

    data-visualization

    Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Every heading is a finding ("Recall drops 18 points below 2 percent prevalence"), not a topic ("Evaluation").

    line 12
  2. Very specific:

    Every control has a description and a tooltip.

    line 35

“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Data Storytelling

Narrative rules

  1. Lead with the decision: the first cell or slide states the takeaway as a sentence with its number.
  2. SCQA: situation, complication, question, resolution and action.
  3. Every heading is a finding ("Recall drops 18 points below 2 percent prevalence"), not a topic ("Evaluation"). In notebooks, compute headings with display(Markdown(f"## ...")) so the numbers are real.
  4. One message per chart. The title states the takeaway, axes carry units, direct labels beat legends, colorblind-safe palette (Okabe-Ito), bar axes start at zero, no dual axes or 3D without need.
  5. End with recommended actions, risks, open questions and next steps, then limitations.
  6. Conditional wording: when a sentence interprets a number, write it so it stays true if the number moves ('rose' if b > a else 'fell').

Widgets (core ipywidgets only)

Templates: templates/notebooks/whatif_dashboard.ipynb (seven panels) and story_report.ipynb.

PanelControlsShows
Threshold explorerthreshold sliderconfusion matrix, precision, recall, F1, expected cost, point on the PR curve
Prevalence explorerprevalence, remedyaccuracy, recall, precision, PR-AUC, ROC-AUC from a precomputed grid
What-if analyzersliders and dropdowns per predictor, resetprobability, label, local contributions, baseline versus scenario table
Cost-matrix editorcost of FP and FNoptimal threshold, expected savings
Cohort filtersegment, time rangemetrics and score distribution
Scenario comparisonname, savetable and chart of saved scenarios
Sensitivity viewpredictorICE-style curve with the training range shaded

Widget engineering

  • interactive_output with Output widgets, or interact_manual for heavy work. continuous_update=False on sliders that call a model. Never refit inside a callback; precompute grids.
  • State in one dictionary or small class.
  • Every control has a description and a tooltip; layouts use HBox, VBox, GridBox with set widths; keyboard reachable.
  • Clamp to the training range; warn on extrapolation.
  • Each panel wraps a pure function; a static fallback cell calls every function with fixed inputs and prints tables, which also proves the callbacks run.

Export

jupyter nbconvert --to html keeps tables and static charts; voila notebook.ipynb serves widgets as a dashboard; widgets need a live kernel. In Colab, core widgets work; enable the custom widget manager only for third-party widgets.

Recipes: references/widgets.md.

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