by kamilstanuch · Claude Skill · ★ 358
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Codebase Digest Codebase Digest is a command-line tool written in Python that helps you analyze and understand your codebase. It provides a structured overview of your project's directory structure, file sizes, token counts, and even consolidates the content of all text-based files into a single output for easy analysis with Large Language Models (LLMs). Table of Contents Features Installation Usage Configuration Ignore Functionality LLM Prompts for Enhanced Analysis Contributing License Features 📊 Directory Tree Visualization: Generate a hierarchical view of your project structure 📈 Codebase Statistics: Calculate total files, directories, code size, and token counts 📄 File Content Consolidation: Combine all text-based files into a single output 🚫 Flexible Ignore System: Support for custom patterns, defaults, and files 🎨 Multiple Output Formats: Choose between text, JSON, Markdown, XML, or HTML 🌈 Colored Console Output: Visually appealing and informative summaries 🧠 LLM Analysis S
| Stars | 358 |
| Forks | 31 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 35.8/100 |
| Open Issues | 4 |
| Last Updated | 2024-10-21 |
| Created | 2024-09-07 |
| Platforms | claude-code, gemini, python |
| Est. Tokens | ~98k |
These tools work well together with codebase-digest for enhanced workflows:
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codebase-digest is 🗜️ Codebase-digest is your AI-friendly codebase packer and analyzer. Features 60+ coding prompts and generates structured overviews with metrics. Ideal for feeding projects to LLMs like GPT-4, Claude. It is categorized as a Claude Skill with 358 GitHub stars.
codebase-digest is primarily written in Python. It covers topics such as ai, anthropic, chatgpt.
You can find installation instructions and usage details in the codebase-digest GitHub repository at github.com/kamilstanuch/codebase-digest. The project has 358 stars and 31 forks, indicating an active community.
The top alternatives to codebase-digest on Agent Skills Hub include c4-genai-suite, py-gpt, tools. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.