programmatic-eda

Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.

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Repository: nimrodfisher/data-analytics-skills
Last source activity: April 25, 2026 at 15:33
Detected SKILL.md language: English
Stars: 403
Forks: 75

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SKILL.md

Source instructions · Read-only preview

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analysis-assumptions-log

Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

2026-04-26

analysis-documentation

Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.

2026-04-26

data-catalog-entry

Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.

2026-04-26

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When to use

You receive a new dataset and need to understand its shape and quality before analysis

An analysis produces surprising numbers and you want to verify the underlying data first

A stakeholder asks "is this data reliable?" or "what's in this table?"

You're about to run a model or statistical test and need data-quality assurance

Process

Load and overview — run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).

Null profile — run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.

Outlier detection — run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?

Distribution summary — run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.

Correlation exploration — run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.

EDA checklist sign-off — work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.

Write findings — fill assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.

For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.