The Sample Text Processor is a simple skill designed to demonstrate the basic structure and functionality expected in the claude-skills ecosystem. This skill provides fundamental text processing capabilities including word counting, character analysis, and basic text transformations.
This skill serves as a reference implementation for BASIC tier requirements and can be used as a template for creating new skills. It demonstrates proper file structure, documentation standards, and implementation patterns that align with ecosystem best practices.
The skill processes text files and provides statistics and transformations in both human-readable and JSON formats, showcasing the dual output requirement for skills in the claude-skills repository.
Features
Core Functionality
Word Count Analysis
Count total words, unique words, and word frequency
Character Statistics
Analyze character count, line count, and special characters
Text Transformations
Convert text to uppercase, lowercase, or title case
File Processing
Process single text files or batch process directories
Dual Output Formats
Generate results in both JSON and human-readable formats
Technical Features
Command-line interface with comprehensive argument parsing
Error handling for common file and processing issues
Progress reporting for batch operations
Configurable output formatting and verbosity levels
Cross-platform compatibility with standard library only dependencies
Usage
Basic Text Analysis
python text_processor.py analyze document.txt
python text_processor.py analyze document.txt
--output
results.json
Text Transformation
python text_processor.py transform document.txt
--mode
uppercase
python text_processor.py transform document.txt
--mode
title
--output
transformed.txt
Batch Processing
python text_processor.py batch text_files/
--output
results/
python text_processor.py batch text_files/
--format
json
--output
batch_results.json
Examples
Example 1: Basic Word Count
$ python text_processor.py analyze sample.txt
==
=
TEXT ANALYSIS RESULTS
==
=
File: sample.txt
Total words:
150
Unique words:
85
Total characters:
750
Lines:
12
Most frequent word:
"the"
(
8
occurrences
)
Example 2: JSON Output
$ python text_processor.py analyze sample.txt
--format
json
{
"file"
:
"sample.txt"
,
"statistics"
:
{
"total_words"
:
150
,
"unique_words"
:
85
,
"total_characters"
:
750
,
"lines"
:
12
,
"most_frequent"
:
{
"word"
:
"the"
,
"count"
:
8
}
}
}
Example 3: Text Transformation
$ python text_processor.py transform sample.txt
--mode
title
Original:
"hello world from the text processor"
Transformed:
"Hello World From The Text Processor"
Installation
This skill requires only Python 3.7 or later with the standard library. No external dependencies are required.
Clone or download the skill directory
Navigate to the scripts directory
Run the text processor directly with Python
cd
scripts/
python text_processor.py
--help
Configuration
The text processor supports various configuration options through command-line arguments:
--format
Output format (json, text)
--verbose
Enable verbose output and progress reporting
--output
Specify output file or directory
--encoding
Specify text file encoding (default: utf-8)
Architecture
The skill follows a simple modular architecture:
TextProcessor Class
Core processing logic and statistics calculation
OutputFormatter Class
Handles dual output format generation
FileManager Class
Manages file I/O operations and batch processing
CLI Interface
Command-line argument parsing and user interaction
Error Handling
The skill includes comprehensive error handling for:
File not found or permission errors
Invalid encoding or corrupted text files
Memory limitations for very large files
Output directory creation and write permissions
Invalid command-line arguments and parameters
Performance Considerations
Efficient memory usage for large text files through streaming
Optimized word counting using dictionary lookups
Batch processing with progress reporting for large datasets
Configurable encoding detection for international text
Contributing
This skill serves as a reference implementation and contributions are welcome to demonstrate best practices:
Follow PEP 8 coding standards
Include comprehensive docstrings
Add test cases with sample data
Update documentation for any new features
Ensure backward compatibility
Limitations
As a BASIC tier skill, some advanced features are intentionally omitted:
Complex text analysis (sentiment, language detection)
Advanced file format support (PDF, Word documents)
Database integration or external API calls
Parallel processing for very large datasets
This skill demonstrates the essential structure and quality standards required for BASIC tier skills in the claude-skills ecosystem while remaining simple and focused on core functionality.