A Claude Code Skill, "Paper X-Ray Scanner" deconstructs academic papers to extract core logical models and distill complex research into concise summaries. It targets researchers and power users seeking to rapidly comprehend academic work by penetrating jargon and identifying key contributions. The tool offers a structured analysis, highlighting innovations and critical boundaries, thereby accelerating the review process.

How It Works

The skill utilizes a "Cognitive Extraction Algorithm" that processes input through denoising, extraction, and critique stages. It performs a five-dimensional analysis, identifying the core problem, the proposed solution mechanism, the novel contribution (innovation increment), the critical boundaries or limitations, and a concise "napkin formula" summarizing the essence. This multi-faceted approach aims to provide a deep yet accessible understanding of research papers.

Quick Start & Requirements

Installation is performed within the Claude Code environment using bash commands: /plugin marketplace add lijigang/ljg-skill-xray-paper /plugin install ljg-xray-paper Usage involves invoking the skill with a paper's PDF path, URL, or pasted content via /ljg-xray-paper <input>. No external prerequisites are specified beyond the Claude Code environment.

Highlighted Details

• Extracts and analyzes core components of academic papers, including problem definition, innovation, and critique.
• Generates reports in Org-mode format, featuring ASCII logic flowcharts and "napkin sketches."
• Key outputs include: NAPKIN FORMULA, PROBLEM, INSIGHT, DELTA (vs. SOTA), and CRITIQUE.
• Accepts input via PDF path, URL, or direct text content.

Maintenance & Community

No information regarding maintenance, community channels, or contributors is provided in the README snippet.

Licensing & Compatibility

The project is licensed under the MIT license. This permissive license allows for broad usage, including commercial applications and integration into closed-source projects.

Limitations & Caveats

The accuracy and depth of the analysis are contingent on the capabilities of the underlying Claude Code AI models in interpreting academic content. The quality of the output may vary depending on the complexity and clarity of the input paper. No specific technical limitations or known bugs are detailed.