Data Cleaning Checklist Generator

Generate a data cleaning checklist for my dataset before I begin analysis.

**What my dataset contains:**
[Describe the data — what it represents, how many rows/columns, time period, how it was collected]

**Data source:**
[e.g. CRM export, database dump, survey platform, web scrape, manual entry, API pull]

**Format:**
[e.g. CSV, Excel, SQL table, JSON, Parquet]

**Known issues I'm already aware of:**
[Any data quality problems you've already spotted]

**Analysis I'm planning:**
[What you'll do with this data — affects what cleaning matters most]

**Tool I'm using:**
[e.g. Python/pandas, R, Excel, SQL, dbt]

Generate a comprehensive data cleaning checklist organized by category:

**Structure checks:**
- Column names, data types, encoding issues

**Completeness checks:**
- Missing values, nulls, empty strings — and what to do with each

**Validity checks:**
- Out-of-range values, impossible dates, format inconsistencies

**Consistency checks:**
- Duplicates, conflicting values, join key integrity

**Relevance checks:**
- Columns to drop, rows to filter, time period to scope

For each check: what to look for, how to detect it in my tool, and the recommended action (remove / impute / flag / investigate).