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
- Profile the dataset shape, column types, and missing-value risk.
- Pick the smallest useful analysis path instead of running every script by default.
- Start with
scripts/data_storyteller.pywhen the user wants a cohesive report. - Reach for focused helpers when the task is narrow:
- data_quality_auditor.py - dataset_comparer.py - correlation_explorer.py - outlier_detective.py - statistical_analyzer.py - survey_analyzer.py - ts_decomposer.py - pivot_table_generator.py - ab_test_calc.py - roi_calculator.py - budget_analyzer.py
- 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.