Data Storyteller
SkillDatabases & dataAnalyze datasets and turn them into narrative reports with charts, audits, comparisons, and statistical summaries. Use for exploratory analysis and executive-ready outputs.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Data Storyteller skill
What this skill tells your AI
The instructions your AI receives, as published by dkyazzentwatwa/chatgpt-skills in data-storyteller/SKILL.md and read by ahel’s review.
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.pydataset_comparer.pycorrelation_explorer.pyoutlier_detective.pystatistical_analyzer.pysurvey_analyzer.pyts_decomposer.pypivot_table_generator.pyab_test_calc.pyroi_calculator.pybudget_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.
Signals
- GitHub stars
- 100
- Forks
- 20
- Last commit
- Apr 2026
Advanced
- Catalog kind
- skill
- Gateway key
data-storyteller- Source
- github.com/dkyazzentwatwa/chatgpt-skills