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analysis-authoring
| Field | Value |
|---|---|
| Kind | skill |
| Source | .github/skills/data-science-engineering/analysis-authoring |
| Invocation | Loaded on demand by referencing agents |
| Interactive | No |
What it does
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.
When to use it
Use this skill when composing or reviewing an exploratory data analysis notebook, an analytical dashboard, or a dashboard validation pass. It supplies the editorial judgment that generic tooling does not carry: which sections belong and in what order, which figure answers which question, how to handle dense or high-cardinality data, and what responsiveness budgets to hold a dashboard to.
Reach for a different asset when the question is about column meaning or entity relationships, which data-catalog owns; about persistence format or dataset versioning, which dataops owns; about accessibility conformance criteria, which the accessibility skill owns; or about evaluating an AI system, which evaluation-design owns.
The Data Science and Engineering Coach loads this skill automatically when the user confirms the analysis job.
Example usage
A user confirms the analysis job and asks for an exploratory notebook over a transactions dataset that includes a timestamp column and a high-cardinality merchant column.
Applying this skill, the notebook opens with the analysis question and a data-assets summary that references the existing profile rather than restating it, moves through structure and quality checks into univariate distributions, then into multivariate relationships.
Because a datetime field exists, the temporal section is included. Because the merchant column has high cardinality, its distribution is shown as a top-N bar chart with the remainder grouped rather than as an unreadable full-cardinality plot. The correlation matrix is fixed to a diverging scale from minus one to one so weak relationships are not visually exaggerated.
Each figure is preceded by the question it answers and followed by an interpretation placeholder, and the notebook closes with written limitations and next steps.