⚡AgentSkills
📊 Data Science & Analytics · Exploratory Analysis

Run a dataset health check before modeling

Profile distributions, missingness, leakage risks, and unit sanity before trusting any result.

foundation~30 minData ScientistsAnalystsResearch Scientists

Steps

  1. 1Check shape, dtypes, and unique counts; flag columns failing expectations
  2. 2Plot distributions for numeric columns; eyeball impossible values
  3. 3Quantify missingness patterns: MCAR vs structured gaps telling stories
  4. 4Hunt leakage: fields created after outcome timestamps
  5. 5Verify joins didn't fan-out rows silently (count before/after)
  6. 6Write a one-page data dictionary others can trust

Common Pitfalls

  • ▲Aggregating across mixed currencies/timezones
  • ▲Imputing before understanding why data is missing

Commands

Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill exploratory-analysis-dataset-health-check
Install globally
$ npx skills add aniruddhaadak80/skills --skill exploratory-analysis-dataset-health-check -g

Tags

#eda#data-quality#data-science#exploratory-analysis

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