csv-data-wrangler
CSV Data Wrangler
Purpose
Provides expertise in efficient CSV file processing, data cleaning, and transformation. Handles large files, encoding issues, malformed data, and performance optimization for tabular data workflows.
When to Use
- Processing large CSV files efficiently
- Cleaning and validating CSV data
- Transforming and reshaping datasets
- Handling encoding and delimiter issues
- Merging or splitting CSV files
- Converting between tabular formats
- Querying CSV with SQL (DuckDB)
Quick Start
Invoke this skill when:
- Processing large CSV files efficiently
- Cleaning and validating CSV data
- Transforming and reshaping datasets
More from 404kidwiz/claude-supercode-skills
frontend-ui-ux-engineer
A designer-turned-developer who crafts stunning UI/UX even without design mockups. Code may be a bit messy, but the visual output is always fire.
2.0Kquant-analyst
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
1.1Kproject-manager
Project management expert specializing in planning, execution, monitoring, and closure of projects. Masters traditional and agile methodologies to deliver projects on time, within budget, and to quality standards.
988machine-learning-engineer
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on reliability and performance.
790dotnet-framework-4.8-expert
Legacy .NET Framework expert specializing in .NET Framework 4.8, WCF services, ASP.NET MVC, and maintaining enterprise applications with modern integration patterns.
724codebase-exploration
Deep contextual grep for codebases. Expert at finding patterns, architectures, implementations, and answering "Where is X?", "Which file has Y?", and "Find code that does Z" questions. Use when exploring unfamiliar codebases, finding specific implementations, understanding code organization, discovering patterns across multiple files, or locating functionality in a project. Supports three thoroughness levels quick, medium, very thorough.
492