Data Storyteller

Use this as the primary analytics skill for structured data. It now absorbs the repo's audit, comparison, statistics, pivot, experiment, and time-series helpers.

Use This For

• Executive summaries and narrative reports from CSV or spreadsheet data
• Data quality audits, comparisons, and anomaly reviews
• Statistical analysis, pivots, experiment reads, ROI and budget analysis
• Survey summaries and time-series decomposition

Workflow

1. Profile the dataset shape, column types, and missing-value risk.
2. Pick the smallest useful analysis path instead of running every script by default.
3. Start with scripts/data_storyteller.py when the user wants a cohesive report.
4. Reach for focused helpers when the task is narrow:

- dataqualityauditor.py - datasetcomparer.py - correlationexplorer.py - outlierdetective.py - statisticalanalyzer.py - surveyanalyzer.py - tsdecomposer.py - pivottablegenerator.py - abtestcalc.py - roicalculator.py - budgetanalyzer.py

1. Translate outputs into plain-English findings, risks, and next actions.

Guardrails

• Do not overstate causal claims from correlations.
• Call out data quality problems before presenting strong conclusions.
• Keep executive summaries short and move method detail behind them.