data-storyteller

Installation
SKILL.md

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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