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data-storyteller

data-storyteller

github.com/dkyazzentwatwaData & databases124230 installsUpdated 29w ago
Repository

About data-storyteller

A structured-data analytics skill for analysts who need reports, audits, comparisons, or statistical analysis. It combines a cohesive reporting workflow with focused scripts for specialized tasks.

Key features

  1. 01Dataset profiling
  2. 02Data quality and anomaly checks
  3. 03Statistical and survey analysis
  4. 04Pivot and time-series analysis
  5. 05Experiment, ROI, and budget analysis

Use cases

  1. 01Create narrative reports from CSV or spreadsheets
  2. 02Review dataset quality and anomalies
  3. 03Analyze experiments, ROI, or budgets
  4. 04Summarize surveys and time series

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill data-storyteller

After installing, type/data-storytellerin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

.DS_Store
agents/
openai.yaml
scripts/
ab_test_calc.py
budget_analyzer.py
correlation_explorer.py
data_converter.py
data_quality_auditor.py
data_storyteller.py
dataset_comparer.py
outlier_detective.py
pivot_table_generator.py
requirements.txt
roi_calculator.py
statistical_analyzer.py
survey_analyzer.py
ts_decomposer.py

Details

From the project README

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.

Comments

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