# Analysis Authoring

Authoring conventions for exploratory data analysis notebooks and analytical...

- Repository: https://github.com/microsoft/hve-core (1591 stars)
- Category: Testing
- License: CC-BY-4.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add microsoft/hve-core --skill analysis-authoring -g -a claude-code -y`
Codex: `npx skills add microsoft/hve-core --skill analysis-authoring -g -a codex -y`
Cursor: `npx skills add microsoft/hve-core --skill analysis-authoring -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/hve-core --skill analysis-authoring -g -a gemini-cli -y`

## Description

Authoring conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Analysis Authoring Conventions

## Goal

Produce exploratory notebooks and analytical dashboards whose structure, visualization choices, and scale handling are deliberate rather than incidental. This skill supplies the editorial judgment that generic file, notebook, terminal, and browser tooling does not carry.

## Flow

1. Confirm the analysis question, the datasets in scope, and the deliverable shape: notebook, dashboard, or both.
2. Read the available profile and objectives artifacts rather than re-deriving column semantics. `data-catalog` owns the profile contract and semantic roles.
3. Select the deliverable's section sequence from the matching reference, including only the conditional sections the data supports.
4. Choose each visualization from its analytical goal, applying the scale thresholds rather than plotting whole datasets by default.
5. Record interpretation next to each figure so a reader learns what the figure answers, not only what it shows.
6. Validate the deliverable against its completeness expectations, and for dashboards apply the responsiveness budgets as project-adjustable defaults.

## Inputs

* The analysis question and intended audience
* Dataset locations and, when available, an existing data profile and declared objectives
* The deliverable shape and its destination
* Any project-specific performance budget that overrides the defaults here

## Success criteria

The rest: https://github.com/microsoft/hve-core/blob/HEAD/.github/skills/data-science-engineering/analysis-authoring/SKILL.md