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Find Replace Task

Overview

The Find Replace Task performs multiple find-and-replace operations on text in a single step. Clean data, normalize formats, remove unwanted characters, convert between formats, or sanitize content by applying sequential text replacements.

When to use this task:

  • Text normalization and standardization
  • Data cleaning and formatting
  • Remove or replace unwanted characters
  • Convert between data formats
  • Sanitize user input
  • Template variable replacement
  • Format conversion (dates, addresses, etc.)
  • Character encoding fixes

Key Features:

  • Multiple find/replace operations in sequence
  • Regex special character escaping
  • Global replacement (all occurrences)
  • Case-sensitive matching
  • Unlimited replacement pairs
  • Variable support
  • Sequential processing

Quick Start

1. Add Find Replace task
2. Enter source text
3. Add find/replace pairs
4. Replacements applied in order
5. Save

Configuration

Source Text

Input: {{task_55001_address}}
Input: {{task_2001_email_body}}
Input: Hello World! Price: $100

The text to perform replacements on.

Find/Replace Pairs

Find: "  " (double space) → Replace: " " (single space)
Find: "\n" → Replace: "<br>"
Find: "$100" → Replace: "$99.99"
Find: "!" → Replace: "."

Add as many pairs as needed. Applied sequentially.

Output Fields

Field Description Example
task_34001_run Success status true
task_34001_run_text Result message Successfully replaced text.
task_34001_output Text after replacements Hi World. Price: $99.99

Real-World Examples

Example 1: Address Normalization

Scenario: Standardize address formats from various sources

Workflow: 1. Form Submission - Address entry 2. Find Replace - Normalize address 3. Edit Client - Update clean address 4. Webhook Out - Validate with postal service

Normalize Address: 

Input: {{task_55001_address}}

Find/Replace Pairs:
1. Find: "  " → Replace: " " (remove double spaces)
2. Find: "Street" → Replace: "St"
3. Find: "Avenue" → Replace: "Ave"
4. Find: "Road" → Replace: "Rd"
5. Find: "Boulevard" → Replace: "Blvd"
6. Find: "Apartment" → Replace: "Apt"
7. Find: "Suite" → Replace: "Ste"
8. Find: "Floor" → Replace: "Fl"
9. Find: "#" → Replace: "No."
10. Find: "  " → Replace: " " (final cleanup)

Example: 

Input:  123  Main  Street  Apartment  4B  
Output: 123 Main St Apt 4B

Update Client: 

Client ID: {{task_15001_client_id}}
Address: {{task_34001_output}}

Example 2: HTML Email Body Cleaning

Scenario: Convert plain text to HTML-safe format for email

Workflow: 1. AI Prompt - Generate email content 2. Find Replace - Convert to HTML 3. Email - Send formatted email

Convert to HTML: 

Input: {{task_38001_email_body}}

Find/Replace Pairs:
1. Find: "&" → Replace: "&amp;"
2. Find: "<" → Replace: "&lt;"
3. Find: ">" → Replace: "&gt;"
4. Find: "\n\n" → Replace: "</p><p>"
5. Find: "\n" → Replace: "<br>"
6. Find: "  " → Replace: " &nbsp;"

Example: 

Input:
Hello John,

Here's your quote:
Product A: $500
Product B: $750

Total: $1,250

Best regards

Output:
Hello John,</p><p>Here's your quote:<br>Product A: $500<br>Product B: $750</p><p>Total: $1,250</p><p>Best regards

Send Email: 

To: {{task_55001_email}}
Subject: Your Quote
Body HTML: <p>{{task_34001_output}}</p>

Example 3: Phone Number Formatting

Scenario: Clean and format phone numbers to consistent style

Workflow: 1. Form Submission - Contact form 2. Find Replace - Remove formatting 3. Phone Formatter - Format to E.164 4. New Client - Create with clean phone

Remove All Formatting: 

Input: {{task_55001_phone}}

Find/Replace Pairs:
1. Find: "(" → Replace: ""
2. Find: ")" → Replace: ""
3. Find: "-" → Replace: ""
4. Find: " " → Replace: ""
5. Find: "." → Replace: ""
6. Find: "+" → Replace: ""
7. Find: "ext" → Replace: ""
8. Find: "x" → Replace: ""

Example: 

Input: (555) 123-4567 ext. 890
Output: 5551234567890

Input: +1-555.123.4567
Output: 15551234567

Then Format: 

Phone Formatter:
Input: {{task_34001_output}}
Country: US
Format: E.164

Example 4: SQL Query Sanitization

Scenario: Clean user input before using in SQL queries

Workflow: 1. Webhook In - API search request 2. Find Replace - Sanitize search term 3. MySQL Query - Safe database search 4. Webhook Out - Return results

Sanitize Input: 

Input: {{task_46001_search_term}}

Find/Replace Pairs:
1. Find: "'" → Replace: "''"
2. Find: "\"" → Replace: ""
3. Find: ";" → Replace: ""
4. Find: "--" → Replace: ""
5. Find: "/*" → Replace: ""
6. Find: "*/" → Replace: ""
7. Find: "DROP" → Replace: ""
8. Find: "DELETE" → Replace: ""
9. Find: "INSERT" → Replace: ""
10. Find: "UPDATE" → Replace: ""

Safe Query: 

SELECT product_id, name, description, price
FROM products
WHERE name LIKE '%{{task_34001_output}}%'
OR description LIKE '%{{task_34001_output}}%'
LIMIT 50

Warning: This is basic sanitization. Always use parameterized queries or proper SQL escaping in production.

Example 5: CSV Data Cleaning

Scenario: Clean CSV data before import

Workflow: 1. Webhook In - CSV data received 2. Find Replace - Clean data 3. Loop - Process each row 4. New Client - Import cleaned records

Clean CSV Data: 

Input: {{task_46001_csv_row}}

Find/Replace Pairs:
1. Find: "\r\n" → Replace: "" (remove Windows line endings)
2. Find: "\n" → Replace: "" (remove Unix line endings)
3. Find: "\"\"" → Replace: "\"" (fix escaped quotes)
4. Find: "N/A" → Replace: ""
5. Find: "NULL" → Replace: ""
6. Find: "null" → Replace: ""
7. Find: "  " → Replace: " " (remove double spaces)
8. Find: " ," → Replace: "," (fix spacing before commas)
9. Find: ", " → Replace: "," (normalize comma spacing)

Example: 

Input: "John  Doe","","N/A","  123 Main St  ","NULL"
Output: "John Doe","","","123 Main St",""

Import Clean Data: 

Company: {{task_34001_output_col_1}}
Email: {{task_34001_output_col_2}}
Phone: {{task_34001_output_col_3}}
Address: {{task_34001_output_col_4}}

Best Practices

Order Matters

Replacements apply sequentially. Earlier changes affect later ones.

Example:
Input: "test TEST Test"

Option 1 (convert then remove):
1. Find: "test" → Replace: "TEST"  # Result: "TEST TEST Test"
2. Find: "TEST" → Replace: ""      # Result: "   Test"
3. Find: "Test" → Replace: ""      # Result: "   "

Option 2 (remove then convert):
1. Find: "test" → Replace: ""      # Result: " TEST Test"
2. Find: "TEST" → Replace: ""      # Result: "  Test"
3. Find: "Test" → Replace: ""      # Result: "  "

Plan order carefully!

Common Patterns

Remove extra whitespace: 

1. Find: "\t" → Replace: " " (tabs to spaces)
2. Find: "  " → Replace: " " (double to single)
3. Find: "  " → Replace: " " (repeat to catch multiple)

URL encoding: 

1. Find: " " → Replace: "%20"
2. Find: "!" → Replace: "%21"
3. Find: "#" → Replace: "%23"
4. Find: "$" → Replace: "%24"

Strip HTML tags: 

1. Find: "<br>" → Replace: "\n"
2. Find: "<p>" → Replace: ""
3. Find: "</p>" → Replace: "\n\n"
4. Find: "<strong>" → Replace: ""
5. Find: "</strong>" → Replace: ""

Edge Cases

  1. Empty find string - Avoid, causes issues
  2. Circular replacements - Don't replace A→B then B→A
  3. Regex special characters - Automatically escaped
  4. Case sensitivity - Always case-sensitive

Troubleshooting

Replacement Not Working

Issue: Text not being replaced

Causes: - Find string doesn't match exactly (case, spacing) - Previous replacement modified text - Special characters not escaped properly

Solution: 

# Check exact match
Code Task: return {debug: input.task_X001_field};

# Try with Formatter first
Formatter: UPPERCASE or LOWERCASE

Wrong Final Output

Issue: Unexpected result after replacements

Cause: Order of operations

Solution: - Review replacement order - Test incremental replacements - Use multiple Find Replace tasks if needed

Partial Replacements

Issue: Only some occurrences replaced

Cause: Previous replacement altered text

Example: 

Input: "cat dog cat cat"

1. Find: "cat" → Replace: "dog"  # "dog dog dog dog"
2. Find: "dog" → Replace: "pet"  # "pet pet pet pet"

All "cat" became "pet" because they were first converted to "dog"

Solution: Be mindful of replacement chains

Special Characters Issues

Issue: Regex characters causing problems

Solution: Task automatically escapes these: 

. * + ? ^ $ { } ( ) | [ ] \

No manual escaping needed.

Frequently Asked Questions

Is matching case-sensitive?

Yes, always case-sensitive. Use Formatter task to normalize case before Find Replace if needed.

Can I use regex patterns?

No, only literal string matching. For regex, use Pattern Regex task.

How many replacements can I add?

No hard limit. Keep practical (10-20 pairs typical).

Does it support multi-line text?

Yes, works with newlines (\n) and any text format.

Can I replace with empty string?

Yes, effectively removes the found text.

What if find string appears in replace string?

Second occurrence won't be replaced again (not recursive). Only original text is searched.

Can I use variables in find/replace strings?

Yes, both support variable replacement from previous tasks.

  • Formatter - Case conversion, trim, padding
  • Pattern Regex - Pattern-based extraction/validation
  • Variable - Store cleaned text for reuse
  • Code Task - Complex text transformations
  • Loop - Apply cleaning to multiple items