Profile and analyze datasets to surface data quality issues and insights.
npx skills add https://github.com/profmikegreene/gotei --skill data-exploration
Please help me install this Agent Skill. Skill: data-exploration Source: https://github.com/profmikegreene/gotei/tree/main/Gotei_Skills/data-exploration Command: npx skills add https://github.com/profmikegreene/gotei --skill data-exploration
Data exploration enables systematic investigation of datasets to uncover patterns, validate quality, and surface insights that inform decision-making across ad-hoc investigations, data quality checks, and hypothesis generation.
Provide a path to a dataset and run the explore_dataset.py script to generate an exploration report and intelligence brief.
High-intent search queries and answers about installing and using this skill.
Data profiling assesses dataset shape, missingness, dtypes, and duplicates to establish a reliability baseline. This Skill computes statistical summaries and identifies data quality issues automatically using pandas and numpy.
Exploratory data analysis visualizations are generated using matplotlib and seaborn to plot distributions, correlations, and trends. This Skill surfaces actionable insights for hypothesis testing directly from your dataset.
You can detect anomalies and validate data quality across unfamiliar datasets by running the exploration script. It profiles missing values, duplicates, and statistical patterns to surface anomalies and quality issues.
Yes, you need pandas, numpy, matplotlib, and seaborn installed in your Python environment to execute data profiling, quality assessment, and visualization generation tasks.
Exploratory data analysis surfaces actionable insights by assembling profiling findings and visualizations into a concise intelligence brief. This brief guides stakeholders on next steps and hypothesis testing.
Automated data quality assessment establishes a reliability baseline through profiling and statistical summaries, but it does not fix underlying data errors. It surfaces missingness and duplicates to guide manual correction.
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