---
name: data-cleaner
display_name: Data Cleaner
description: Clean messy datasets — handle missing values, fix dtypes, remove duplicates, normalize formats, and flag anomalies.
category: Data & Analytics
platforms: [claude.ai, claude-code, api]
version: 1.0.0
author: Claude Code
tags: [data-cleaner]
---

# Data Cleaner

## Overview

Clean messy datasets — handle missing values, fix dtypes, remove duplicates, normalize formats, and flag anomalies.

## When to Use This Skill

Use **Data Cleaner** when you need to:

- Work with data cleaner tasks in your project or workflow
- Automate data cleaner operations at scale
- Generate production-quality data cleaner output quickly

## Instructions

When this skill is active, Claude will:

1. Understand the full context of your data cleaner request
2. Apply best practices and conventions for Data & Analytics
3. Produce clean, well-structured, production-ready output
4. Explain key decisions and offer alternatives where relevant

## Examples

### Example 1 — Basic Usage

**User:** Help me get started with data cleaner.

**Claude:** I'll walk you through the essential steps for data cleaner in your context...

### Example 2 — Advanced Usage

**User:** I need a production-ready data cleaner setup with full error handling.

**Claude:** Here's a complete, production-hardened data cleaner implementation...

## Guidelines

- Always validate inputs before processing
- Follow the conventions of the target platform or language
- Prefer explicit over implicit — clarity beats cleverness
- Include comments for non-obvious logic
- Suggest tests or validation steps where appropriate

## Dependencies

Required: python, pandas

## Platforms

Available on: claude.ai, claude-code, api

---

*Part of the [claude-skills](https://github.com/inbharatai/claude-skills) collection — 183+ skills for Claude.*