| name | data-cleaning |
| description | Clean datasets with missing value handling, outlier detection, and normalization |
| category | Intelligence & General |
Data Cleaning
Overview
Clean datasets with missing value handling, outlier detection, and normalization. This skill helps you apply structured, repeatable methods for consistent results.
When to Use
- When you need to apply data cleaning best practices
- When establishing processes or standards for your workflow
- When training team members on data cleaning
- When automating or optimizing data cleaning tasks
Instructions
- Assess: Evaluate the current state, requirements, and constraints
- Plan: Define the approach, steps, and success criteria
- Execute: Follow the established patterns and best practices
- Verify: Check results against expected outcomes and quality standards
- Iterate: Refine based on feedback and lessons learned
Examples
User: Help me apply data cleaning for my current project
Assistant: I'll help you apply data cleaning step by step...
Related Skills
- Use with
workflow tool for orchestrated execution
- Combine with
task tool for delegated processing
- Reference
system-prompt for system-level integration