Agent skill · data analytics · jeremylongshore
preprocessing-data-with-automated-pipelines
Process automate data cleaning, transformation, and validation for ML
Why this skill is useful
Adds executable Python scripts for automated data preprocessing pipelines that streamline ETL processes and ensure data quality.
What it needs
Requires numpy, pandas, python installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Data Preprocessing Pipeline Construct and execute automated data preprocessing pipelines for cleaning, transforming, and validating ML-ready datasets. Overview construct and execute automated data preprocessing pipelines, ensuring data quality and readiness for machine learning. It streamlines the data preparation process by automating common tasks such as data cleaning, transformation, and validation. How It Works 1. Analyze Requirements: Claude analyzes the user's request to understand the specific data preprocessing needs, including data sources, target format, and desired transformations. 2. Generate Pipeline Code: Based on the requirements, Claude generates Python code for an automated data preprocessing pipeline using relevant libraries and best practices. This includes data validation and error handling. 3. Execute Pipeline: The generated code is executed, performing the data preprocessing steps. 4. Provide Metrics and Insights: Claude provides performance metrics and insights about the pipeline's execution, including data quality reports and potential issues encountered. When to Use This Skill This skill activates when you need to: Prepare raw data for machine learning models. Automate data cleaning and transformation processes. Implement a robust ETL (Extract, Transform, Load) pipeline. Examples Example 1: Cleaning Customer Data User request: "Preprocess the customer data from the CSV file to remove duplicates and handle missing values." The skill will: 1. Generate a Python script to read the CSV file, remove duplicate entries, and impute missing values using appropriate techniques (e.g., mean imputation). 2. Execute the script and provide a summary of the changes made, including the number of duplicates removed and the number of missing values imputed. Example 2: Transforming Sensor Data User request: "Create an ETL pipeline to transform the sensor data from the database into a format suitable for time series analysis." The skill will: 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/preprocessing-data-with-automated-pipelines--05e70a