| 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
- Identify raw inputs and preserve them unchanged.
- Define the target output format and location.
- Classify issues: missing values, duplicates, type errors, invalid categories, outliers, date problems, and inconsistent identifiers.
- Decide handling rules and document assumptions.
- Implement cleaning in a reproducible script if requested.
- Write cleaned outputs to
outputs/ or a task-specific directory, not raw/.
- 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:
- Cleaning issues found
- Cleaning rules
- Files/scripts created
- Before/after validation
- Residual risks