data-analysis

Use this skill when asked to explore, analyse, or visualise any dataset, CSV, table, or collection of numbers.

Analysis Workflow

Always follow this sequence:

Inventory - How many rows? How many columns? What are the column names and types?
Quality check - Missing values, duplicates, outliers, type mismatches.
Descriptive stats - Mean, median, std, min, max, percentiles for numeric columns.
Distribution - Are numeric columns skewed? Are categorical columns imbalanced?
Relationships - Correlations, group comparisons, time trends if a date column exists.
Key findings - The 3-5 most actionable insights from the above.

Never skip steps. Never jump to "Key findings" without the groundwork.

Every insight must include a number. "Higher" without a figure is not an insight.
Flag data quality issues before drawing conclusions - dirty data produces wrong insights.
Distinguish correlation from causation explicitly when it matters.
If asked to plot or chart, use the scripts in scripts/ - do not write raw matplotlib from scratch.
If a column looks like a date but is typed as a string, note it and parse it before time-series analysis.

Pre-built analysis scripts live in scripts/. Read them before writing any analysis code.

Script	Purpose
`scripts/profile.py`	Full dataset profile: types, nulls, stats, top values
`scripts/correlations.py`	Pearson + Spearman correlation matrix with heatmap
`scripts/time_series.py`	Date-aware trend analysis, resampling, rolling averages

Usage: copy the relevant script, adapt the INPUT_FILE and column name variables at the top, then run.