data-cleaning

name	data-cleaning
description	Use when planning safe data cleaning, deduplication, missing-value handling, anomaly handling, and reproducible cleaned outputs.
metadata	{"version":"0.1","updated":"2026-05-11"}

Data Cleaning Skill

Use this workflow for data cleaning plans or scripts.

Steps

1. Identify raw inputs and preserve them unchanged.
2. Define the target output format and location.
3. Classify issues: missing values, duplicates, type errors, invalid categories, outliers, date problems, and inconsistent identifiers.
4. Decide handling rules and document assumptions.
5. Implement cleaning in a reproducible script if requested.
6. Write cleaned outputs to outputs/ or a task-specific directory, not raw/.
7. Validate before/after row counts, key counts, missingness, and edge cases.

Do not

• Do not overwrite raw files.
• Do not silently drop rows or impute values without documenting rules.
• Do not treat outliers as errors without evidence.

Output

Return:

1. Cleaning issues found
2. Cleaning rules
3. Files/scripts created
4. Before/after validation
5. Residual risks