skills.rest
tanaer
tanaerCOMMUNITY
Explore all skills by this author →
GitHub

data-cleaning

Clean and transform raw data with JavaScript functions for filtering, mapping, and validating rows.

Updated Aug 23, 2026
Search Tags:#data quality#data validation#data transformation#etl#data cleaning#missing values
One-click install
npx skills add https://github.com/tanaer/lobster-university-v2 --skill data-cleaning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-cleaning
Source: https://github.com/tanaer/lobster-university-v2/tree/main/skills/courses/data-cleaning
Command: npx skills add https://github.com/tanaer/lobster-university-v2 --skill data-cleaning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you clean messy data, standardize formats, and ensure data quality, making your datasets reliable and ready for analysis.

Core Features & Use Cases

  • Handle Missing Values: Identify and process missing data points using various strategies.
  • Standardize Formats: Normalize dates, numbers, and text to consistent formats.
  • Deduplication & Validation: Remove duplicate entries and validate data against defined rules.
  • Use Case: You have a CSV file with inconsistent date formats and some missing phone numbers. This Skill can clean it up by standardizing dates to YYYY-MM-DD and filling missing numbers with "N/A".

Quick Start

Use the data-cleaning skill to process the attached customer data CSV, handling missing phone numbers and standardizing date formats.

Frequently Asked Questions about data-cleaning

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

FAQPage Schema
How do I clean messy CSV data and handle missing values before analysis?▼

To clean messy CSV data, you can process missing values using various strategies, standardize inconsistent formats, and remove duplicates to ensure your dataset is reliable and ready for analysis.

What is the best way to standardize inconsistent date formats in raw data?▼

Standardizing inconsistent date formats involves normalizing your raw data rows to a consistent pattern like YYYY-MM-DD using mapping and validation rules during your data preparation process.

Can I use JavaScript to validate data rows and perform deduplication in an ETL process?▼

Yes, you can use JavaScript functions for filtering, mapping, and validating data rows based on specified rules to perform deduplication and ensure data integrity within ETL processes.

Does data transformation require specific strategies for filling missing phone numbers?▼

Data transformation for missing phone numbers requires specifying a strategy, such as filling the missing data points with a default value like N/A to maintain dataset completeness.

What are the limitations of using JavaScript for data validation and format standardization?▼

The primary limitation is that data validation and format standardization depend entirely on your specified JavaScript rules, meaning you must manually define the filtering and mapping logic for your specific data quality requirements.

Related Skills

Explore More Data & Analytics Skills→
xcrrrxcrrr
4

data-cleaner

Clean raw datasets by fixing missing values, duplicates, and formats.

Community
Advanced
elophantoelophanto
116

analytics-reporting

Analyzes raw data to build dashboards, reports and KPI tracking using SQL and Python/R.

Community
Advanced
duggal1duggal1
2

data-pipeline

Automates ETL/ELT workflows with counting, transformation, verification, and monitoring phases.

Community
Advanced
nimadorostkarnimadorostkar
25

data-quality

Implement data validation gates for automated pipelines using Pandera or Great Expectations.

Community
Advanced