Does this skill require any external Python libraries?

No, it is built entirely using the Python Standard Library, ensuring it runs on any system with Python 3.7+ without extra installation steps.

Can I use this skill to process multiple files at once?

Yes, the skill includes a batch processing mode that can recursively scan and process entire directories.

What kind of transformations can it perform?

It currently supports converting text to uppercase, lowercase, and title case, which is useful for normalizing headers or file content.

What is the Sample Text Processor skill?

It is a specialized Claude Code skill that provides text analysis and transformation features, serving as a reference for building BASIC tier plugins.

Is the output compatible with other automation tools?

Yes, the skill can output results in JSON format, making it easy to pipe data into other scripts or software workflows.

Sample Text Processor

Name: Sample Text Processor
Author: alirezarezvani

byalirezarezvani

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9,958

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Content Management

Provides fundamental text analysis and transformation capabilities for processing files and directories through Claude Code.

The Sample Text Processor is a versatile utility designed for the Claude Skills ecosystem that enables efficient text analysis and automated transformations. It allows users to generate detailed word and character statistics, perform batch transformations such as case conversion, and produce structured outputs in both human-readable and JSON formats. Serving as a reference implementation for BASIC tier skills, it demonstrates best practices in modular architecture, error handling, and cross-platform compatibility, making it an ideal tool for developers who need reliable text processing without external dependencies.

Key Features

01Zero-dependency implementation using only the Python Standard Library

02Automated text transformations including title, upper, and lower case

03Comprehensive word frequency and character statistics analysis

04Dual output generation in structured JSON and human-readable formats

05Batch processing support for entire directories with progress reporting

069,958 GitHub stars

Use Cases

01Extracting structured metadata from text assets for automated data pipelines

02Generating statistical reports for large batches of documentation or logs

03Standardizing text formatting across multiple source files during development

Key Features

01Zero-dependency implementation using only the Python Standard Library

02Automated text transformations including title, upper, and lower case

03Comprehensive word frequency and character statistics analysis

04Dual output generation in structured JSON and human-readable formats

05Batch processing support for entire directories with progress reporting

069,958 GitHub stars

Use Cases

01Extracting structured metadata from text assets for automated data pipelines

02Generating statistical reports for large batches of documentation or logs

03Standardizing text formatting across multiple source files during development