mcp-data-cleaning

Automates data cleaning and preprocessing for MCP-driven clinical research datasets.

2|Updated Jan 26, 2026

Search Tags:#workflow #preprocessing #data-quality #data-cleaning #categorical-encoding #missing-values #vif

Ready to run

npx skills add https://github.com/u9401066/automl-stat-mcp --skill mcp-data-cleaning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mcp-data-cleaning
Source: https://github.com/u9401066/automl-stat-mcp/tree/main/.claude/skills/mcp-data-cleaning
Command: npx skills add https://github.com/u9401066/automl-stat-mcp --skill mcp-data-cleaning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data cleaning and preprocessing are essential to unlock reliable insights from messy clinical and research datasets. This skill provides a standard MCP-driven workflow to diagnose data issues, handle missing values, encode categoricals, filter rows, remove irrelevant columns, and check multicollinearity, enabling consistent preparation for downstream analyses.

Core Features & Use Cases

Diagnose data quality and column information to guide cleaning decisions.
Apply flexible missing-value strategies (mean, median, mode, constant, or drop) per column.
Encode categorical variables, filter rows, remove columns, and perform VIF checks to ensure model-ready data.
Use Case: Prepare Titanic-like datasets for ML modeling with transparent, repeatable steps.

Quick Start

Run the data cleaning workflow on your raw dataset to produce a cleaned dataset ready for modeling.

Frequently Asked Questions about mcp-data-cleaning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I handle missing values in clinical research datasets for machine learning?▼

To prepare clinical research datasets for modeling, use a structured data cleaning workflow that diagnoses data quality, handles missing values, encodes categoricals, filters rows, and checks VIF. This produces model-ready data with documented, reproducible steps.

What is the best way to check multicollinearity and VIF before modeling a dataset?▼

Checking VIF to detect multicollinearity is a core data preprocessing step that removes redundant features before modeling. A structured cleaning workflow checks VIF alongside encoding categoricals and filtering rows to ensure safe, model-ready data.

How do I encode categorical variables and remove irrelevant columns in a reproducible workflow?▼

Encoding categorical variables and removing irrelevant columns are configurable steps within a standardized data cleaning workflow. This approach documents each transformation, enabling reproducibility and safe data preparation for MCP-driven analyses.

Can I use a single data cleaning workflow for datasets with mixed data quality issues?▼

Yes, a standardized MCP-driven data cleaning workflow can diagnose mixed data quality issues across clinical datasets. It applies configurable missing-value strategies, filters rows, and removes columns to produce consistent, model-ready data.

Does the data cleaning workflow require any specific dependencies or components to run?▼

No specific dependencies or components are required to run this data cleaning workflow. It operates as a standalone skill to diagnose data issues, handle missing values, and check VIF for model-ready data.