data-storyteller
Analyze datasets and turn them into narrative reports with charts, audits, comparisons, and statistical summaries. Use for exploratory analysis and executive-ready outputs.
What this skill does
# 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: - `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` 5. 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.
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