data-cleaning-brieflisted

# Data Cleaning Brief ## What This Skill Does Writes clear, step-by-step instructions for cleaning a messy or inconsistent dataset — specifying exactly what needs to be standardised, corrected, or removed to make the data ready for analysis and publication. ## When To Use This Skill - You have received a dataset that is clearly messy (inconsistent formats, duplicates, blank fields, mixed naming conventions) and need to brief a data analyst or developer on how to clean it - You want to document your cleaning decisions for editorial transparency and methodological reproducibility - You are cleaning data yourself and want a structured checklist to work through - You are handing off a partially cleaned dataset and need to document what has been done and what remains ## What You Need To Provide **Required:** A description of the dataset and the problems you can see in it — specific examples of inconsistent values, formatting problems, missing data, or structural issues. Column names and a small sample of the messy rows. **Optional:** The intended analysis goal (what you will do with the data once it is clean); any cleaning decisions that have already been made; the tool the analyst will use (Excel, Python, R, SQL); the deadline. ## How the Assistant Approaches This 1. Identifies each cleaning problem from the description and categorises it: standardisation (format, spelling, case), deduplication, missing value handling, type conversion, or structural issues. 2. Writes a number