Agent skill · data analytics · aj-geddes
Data Cleaning Pipeline
Build robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pipeline automation
Why this skill is useful
Provides specific data cleaning strategies and Python scripts for handling missing values, outliers, and data transformation that the AI wouldn't reliably generate on its own.
What it needs
Requires pandas, scikit-learn installed locally. About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Data Cleaning Pipeline Overview Data cleaning pipelines transform raw, messy data into clean, standardized formats suitable for analysis and modeling through systematic handling of missing values, outliers, and data quality issues. When to Use Preparing raw datasets for analysis or modeling Handling missing values and data quality issues Removing duplicates and standardizing formats Detecting and treating outliers Building automated data preprocessing workflows Ensuring data integrity and consistency Core Components Missing Value Handling: Imputation and removal strategies Outlier Detection & Treatment: Identifying and handling anomalies Data Type Standardization: Ensuring correct data types Duplicate Removal: Identifying and removing duplicates Normalization & Scaling: Standardizing value ranges Text Cleaning: Handling text data Cleaning Strategies Deletion: Removing rows or columns Imputation: Filling with mean, median, or predictive models Transformation: Converting between formats Validation: Ensuring data integrity rules Implementation with Python Pipeline Architecture Advanced Cleaning Techniques Key Decisions How to handle missing values (delete vs impute)? Which outliers are legitimate business cases? What are acceptable value ranges? Which duplicates are true duplicates? How to standardize categorical values? Validation Steps Check for data type consistency Verify value ranges are reasonable Confirm no unintended data loss Document all transformations applied Create audit trail of changes Deliverables Cleaned dataset with quality metrics Data cleaning log documenting all steps Validation report confirming data integrity Before/after comparison statistics Cleaning code and pipeline documentation
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/data-cleaning-pipeline