Summary

This project, ljg-skill-xray-book, addresses the challenge of extracting actionable, learnable structures from books. It functions as a Claude Code Skill, employing a novel "three-round cognitive compression" methodology to distill complex texts into digestible formats for users seeking deep knowledge acquisition. The primary benefit is maximizing the extraction of transferable insights from any given book.

How It Works

The core approach leverages a multi-stage cognitive compression process inspired by the Epiplexity principle. It performs a "Skeleton Scan" to identify the book's global structure, followed by a "Flesh Dissection" to analyze argument chains and supporting evidence. The final "Soul Extraction" phase aims to identify cross-domain migration potential for the learned concepts. Additionally, a "Napkin Compression" offers an extreme summary format, distilling key takeaways into a formula, a sketch, and a single sentence.

Quick Start & Requirements

Installation is managed through the Claude Code plugin system. Users first add the plugin to their marketplace via bash /plugin marketplace add lijigang/ljg-skill-xray-book, then install it with /plugin install ljg-xray-book. Usage involves invoking the plugin within Claude Code using the command /ljg-xray-book "Book Title". The primary prerequisite is an active Claude Code environment.

Highlighted Details

• Implements a unique three-round cognitive compression: Skeleton Scan, Flesh Dissection, and Soul Extraction.
• Generates a highly condensed "Napkin" summary including a formula, ASCII sketch, and a one-sentence distillation.
• Outputs comprehensive reports in Org-mode format, complete with an ASCII structure map of the entire book.
• Supports input via book title, direct content, or URLs.

Maintenance & Community

No specific details regarding contributors, sponsorships, community channels (like Discord/Slack), or roadmap were provided in the README.

Licensing & Compatibility

The project is released under the MIT license. This permissive license generally allows for broad use, including integration into commercial and closed-source applications without significant restrictions.

Limitations & Caveats

The effectiveness of the knowledge extraction is inherently tied to the AI model's interpretation capabilities within the Claude Code environment. The project does not specify handling for extremely large texts or particular content formats, and its reliance on AI means potential for subjective interpretation or inaccuracies in the generated summaries.