n8n-nodes-flowengine-data-standardize-clean

Stop writing Code Nodes to clean your data. Use this.

A production-ready n8n Community Node that cleans and transforms messy data without writing a single line of code. Built with zero runtime dependencies for maximum compatibility and verification compliance.

The Problem

You're building automations in n8n, and your data is a mess:

• Names like jOhN dOE instead of John Doe
• Phone numbers in 15 different formats: (555) 000-1111, 555.000.1111, +1 555 000 1111
• Duplicate contacts that are almost the same but not quite
• Emails with random whitespace and uppercase letters
• JSON with inconsistent key naming (firstName, first_name, FirstName)

The old way: Write complex JavaScript in Code Nodes, debug for hours, maintain spaghetti code.

The new way: Drop in the Data Cleaner node, configure in 30 seconds, done.

Installation

Via n8n Community Nodes

1. Go to Settings > Community Nodes
2. Select Install
3. Enter n8n-nodes-flowengine-data-standardize-clean
4. Click Install

Manual Installation

# In your n8n installation directory
pnpm install n8n-nodes-flowengine-data-standardize-clean

Operations

1. Deduplicate (Fuzzy)

Remove duplicate records using intelligent fuzzy matching. Perfect for cleaning contact lists, product catalogs, or any dataset with near-duplicates.

How it works:

• Uses Jaro-Winkler algorithm for short strings (names, emails)
• Uses Levenshtein distance for longer text
• Configurable similarity threshold (0.0 - 1.0)
• Compares across multiple fields simultaneously

Example:

Input:
  { "name": "John Smith", "email": "[email protected]" }
  { "name": "Jon Smith", "email": "[email protected]" }   // Typo in name
  { "name": "Jane Doe", "email": "[email protected]" }

Output (threshold: 0.8):
  { "name": "John Smith", "email": "[email protected]" }
  { "name": "Jane Doe", "email": "[email protected]" }

Parameters: | Parameter | Description | Default | |-----------|-------------|---------| | Fields to Check | Comma-separated field names to compare | Required | | Fuzzy Threshold | Similarity threshold (0.0 - 1.0) | 0.8 | | Output Duplicate Info | Include metadata about removed duplicates | false |

2. Clean Phone Numbers

Format phone numbers to E.164 international standard (+15550001111). Works with any input format.

Handles:

• (555) 000-1111 → +15550001111
• 555.000.1111 → +15550001111
• +44 20 7946 0958 → +442079460958
• 07946 0958 (UK) → +447946095800

Parameters: | Parameter | Description | Default | |-----------|-------------|---------| | Phone Field | Field containing phone number | phone | | Default Country Code | Country code when not detected | 1 (US/Canada) | | Output Field | Save to different field (optional) | Same as input |

3. Smart Capitalization

Convert text to proper Title Case with intelligent handling of common patterns.

Examples:

• jOhN dOE → John Doe
• ACME CORPORATION → Acme Corporation
• mcdonald's → Mcdonald's

Handles exceptions:

• Preserves acronyms: IBM, NASA, CEO
• Lowercase articles: The Lord of the Rings
• Roman numerals: Henry VIII

Parameters: | Parameter | Description | Default | |-----------|-------------|---------| | Fields to Capitalize | Comma-separated field names | Required |

4. Normalize Email

Clean and standardize email addresses.

What it does:

• Trims whitespace
• Converts to lowercase
• Corrects common domain typos (gmial.com → gmail.com)

Example:

• [email protected] → [email protected]
• [email protected] → [email protected]

Parameters: | Parameter | Description | Default | |-----------|-------------|---------| | Email Field | Field containing email | email | | Output Field | Save to different field (optional) | Same as input |

5. Clean Object Keys

Transform all JSON keys to consistent naming convention.

Modes:

• snake_case: firstName → first_name
• camelCase: first_name → firstName

Features:

• Recursively processes nested objects
• Handles arrays of objects
• Preserves values

Example (snake_case):

// Before
{ "firstName": "John", "contactInfo": { "phoneNumber": "555-1234" } }

// After
{ "first_name": "John", "contact_info": { "phone_number": "555-1234" } }

Parameters: | Parameter | Description | Default | |-----------|-------------|---------| | Key Format | Target case format | snake_case |

Why Zero Dependencies?

This node is built with zero runtime dependencies by design:

1. Verification Ready: Meets n8n Community Node verification requirements
2. Security: Smaller attack surface, no supply chain vulnerabilities
3. Performance: No bloat from unused library features
4. Compatibility: Works across all n8n versions
5. Reliability: No breaking changes from upstream dependencies

All algorithms (Jaro-Winkler, Levenshtein, phone parsing, etc.) are implemented natively in TypeScript with comprehensive documentation.

Nested Field Support

All operations support dot notation for nested fields:

contact.personal.firstName
address.phone.mobile
user.emails[0]

Development

# Install dependencies
pnpm install

# Build the node
pnpm build

# Watch for changes
pnpm dev

# Lint and format
pnpm lint
pnpm format

Testing with n8n

# Link for local development
pnpm link --global

# In your n8n installation
pnpm link --global n8n-nodes-flowengine-data-standardize-clean

# Start n8n
n8n start

License

MIT - Use it however you want.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit PRs to the GitHub repository.

Support

• Issues: GitHub Issues
• Documentation: n8n Community Nodes Docs

Built with care for the n8n community by FlowEngine.