txt

name	txt
description	Use this skill any time a text file (.txt) is the primary input or output. This includes tasks to: read, analyze, search, transform, or create plain text files; extract text patterns; process logs; create reports; manipulate text content. If the user mentions a text file or .txt path, use this skill.

Text File Handling in EverFern

Overview

Users may ask you to read, analyze, create, or modify text files. Use Python's file operations for reading/writing text on Windows with absolute paths (e.g., C:\Users\Username\Downloads\data.txt).

Reading Text Files

Read Entire File

# Read all content
with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    content = f.read()
    print(content)

# Read line by line
with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    for line in f:
        print(line.strip())

# Read into list
with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()
    print(f"Total lines: {len(lines)}")

Writing Text Files

Create or Overwrite File

content = "Hello, World!\nLine 2\nLine 3"

with open(r'C:\path\to\output.txt', 'w', encoding='utf-8') as f:
    f.write(content)

Append to File

with open(r'C:\path\to\file.txt', 'a', encoding='utf-8') as f:
    f.write("\nNew line appended")

Write Multiple Lines

lines = [
    "Line 1",
    "Line 2",
    "Line 3",
    "Line 4"
]

with open(r'C:\path\to\output.txt', 'w', encoding='utf-8') as f:
    f.writelines([line + '\n' for line in lines])

Text Processing

Search and Replace

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    content = f.read()

# Simple replace
new_content = content.replace('old_text', 'new_text')

# Using regular expressions
import re
new_content = re.sub(r'pattern', 'replacement', content)

with open(r'C:\path\to\output.txt', 'w', encoding='utf-8') as f:
    f.write(new_content)

Extract Specific Lines

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()

# Get lines 5-10
selected_lines = lines[4:10]  # Zero-indexed

# Get lines matching pattern
import re
pattern = re.compile(r'keyword')
matching_lines = [line for line in lines if pattern.search(line)]

with open(r'C:\path\to\output.txt', 'w', encoding='utf-8') as f:
    f.writelines(matching_lines)

Count Words, Lines, Characters

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    content = f.read()
    lines = content.splitlines()
    words = content.split()
    chars = len(content)

print(f"Lines: {len(lines)}")
print(f"Words: {len(words)}")
print(f"Characters: {chars}")

Data Extraction

Extract Structured Data

import re

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    content = f.read()

# Extract emails
emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', content)

# Extract URLs
urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', content)

# Extract numbers
numbers = re.findall(r'\d+', content)

print(f"Emails found: {emails}")
print(f"URLs found: {urls}")
print(f"Numbers found: {numbers}")

Parse Delimited Data

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()

# Parse pipe-delimited data
data = []
for line in lines:
    fields = [field.strip() for field in line.split('|')]
    data.append(fields)

print(data)

File Merging and Splitting

Merge Multiple Text Files

import os

files = [
    r'C:\path\to\file1.txt',
    r'C:\path\to\file2.txt',
    r'C:\path\to\file3.txt'
]

with open(r'C:\path\to\merged.txt', 'w', encoding='utf-8') as outfile:
    for file in files:
        with open(file, 'r', encoding='utf-8') as infile:
            outfile.write(infile.read())
            outfile.write('\n')  # Add separator

Split File by Size

def split_file(file_path, lines_per_file=1000):
    with open(file_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()

    for i in range(0, len(lines), lines_per_file):
        chunk = lines[i:i + lines_per_file]
        output_file = f'part_{i//lines_per_file}.txt'

        with open(output_file, 'w', encoding='utf-8') as f:
            f.writelines(chunk)

split_file(r'C:\path\to\large_file.txt')

Text Analysis

Word Frequency

from collections import Counter

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    content = f.read().lower()
    words = content.split()
    filtered_words = [w for w in words if len(w) > 3]  # Skip short words

word_counts = Counter(filtered_words)
print(word_counts.most_common(20))  # Top 20 words

Unique Lines

with open(r'C:\path\to\file.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()

unique_lines = list(set(lines))

with open(r'C:\path\to\output.txt', 'w', encoding='utf-8') as f:
    f.writelines(unique_lines)

Tips and Best Practices

• Always specify encoding: Use encoding='utf-8' for Windows compatibility
• Use context managers: Always use with statements for proper file closure
• Handle errors: Wrap file operations in try/except blocks
• Large files: For very large files, read line-by-line instead of loading all at once
• Path handling: Use raw strings (r'...') for Windows paths to avoid escape issues
• Line endings: Be aware of different line endings (LF vs CRLF on Windows)
• Permissions: Ensure write permissions before trying to create/modify files