| name | data-cleaning |
| description | Clean and transform raw data for analysis. Triggers on 'clean data', 'fix data quality', 'normalize data', 'preprocess data'. NOT for data collection — use data-collection. |
| allowed-tools | Bash, Read, Write, Edit |
When to Use
- Raw data has quality issues (nulls, duplicates, inconsistent formats)
- Data types need conversion or normalization before analysis
- Merging datasets that need schema alignment
- Preparing data for a downstream analysis or visualization pipeline
When NOT to Use
- Data has not been collected yet — use data-collection first
- You need to analyze clean data — use data-analysis
- You need narrative text editing — use a writing/editing skill
Instructions
- Load the dataset from the path or source specified in
$ARGUMENTS.
- Profile the data to identify quality issues:
- Count nulls and missing values per column
- Detect duplicate rows
- Check for format inconsistencies (dates, currencies, encodings)
- Identify outliers and invalid ranges
- Apply cleaning operations in order:
- Remove or flag exact duplicate rows
- Handle missing values (drop, impute with mean/median/mode, or forward-fill as appropriate)
- Fix data types (strings to numbers, date parsing, boolean normalization)
- Normalize text fields (trim whitespace, lowercase, remove special characters)
- Standardize formats (ISO dates, consistent currency, uniform units)
- Validate the cleaned output:
- Confirm no remaining nulls in required fields
- Verify record count delta is expected (document removals)
- Spot-check transformed values against originals
- Write the cleaned dataset to the output location.
- Produce a cleaning report documenting every change.
Output
## Data Cleaning Report
### Input
- File: [path]
- Records: [count]
- Columns: [count]
### Issues Found & Actions Taken
| Issue | Count | Action |
|-------|-------|--------|
| ... | ... | ... |
### Output
- File: [path]
- Records: [count after cleaning]
- Quality score: [percentage of clean rows]
