spunt

Data Visual Explainer — Data skill for Claude Code

Data community

Explain one metric so non-specialists read it correctly and specialists can reproduce it.

How to install Data Visual Explainer

This entry records only its repository, not the path inside it, so there is no exact command to give. Open spunt/data-visual-explainer and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Data Visual Explainer does

Explain one metric so non-specialists read it correctly and specialists can reproduce it. Your agent traces the number to its code, flags where the docs and the code disagree, and writes a Metric Card. An agent skill plus a small CLI.

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README

data-visual-explainer

**Explain one number so non-specialists read it correctly and specialists can reproduce it.**

Point your AI agent at an analysis: a readout, the SQL or notebook behind it, a results table. The agent traces the headline number to the code that produced it, records every place where the write-up and the code disagree, and hands back a **Metric Card**, one source file rendered as an interactive explainer, a one-page label and a methods section.

[![tests](https://github.com/spunt/data-visual-explainer/actions/workflows/tests.yml/badge.svg)](https://github.com/spunt/data-visual-explainer/actions/workflows/tests.yml) [![license: Apache-2.0](https://img.shields.io/badge/license-Apache--2.0-blue)](LICENSE)

[![A Metric Card for an A/B test: +0.21 active days, plausible range +0.05 to +0.37 days, labelled "Shows a link", not a cause](docs/screenshot.png)](https://www.bobspunt.com/data-visual-explainer/site/home-v2-active-days.html)

**[Open the live demo](https://www.bobspunt.com/data-visual-explainer/site/)**: four cards, each a single HTML file that works offline.

At a glance


Use it when	people misquote or misread a number; someone asks what a metric actually measures (who is counted, over what time window, what counts); you need to define or document a KPI, check a metric's written definition against its SQL or code, or write the methods behind a reported figure
Not for	building charts or dashboards, or persuasive slides and narratives
You give it	a folder with the analysis: readout, SQL or notebook, results table, any docs. The more of the pipeline it can read, the more it can check; with only a readout, it marks what it cannot verify instead of guessing
You get back	`cards/.yaml`, the single source of truth, rendered as an HTML explainer, a one-page "Metric Facts" label and a Markdown methods section
Runs in	any agent that reads `SKILL.md` skills (tested in GitHub Copilot