jar-analyzer

🥈Silver

Jar Analyzer - 一个 JAR 包 GUI 分析工具，方法调用关系搜索，方法调用链 DFS 算法分析，模拟 JVM 的污点分析验证 DFS 结果，字符串搜索，Java Web 组件入口分析，CFG 程序分析，JVM 栈帧分析，自定义表达式搜索，紧跟 AI 技术发展，支持 MCP 调用，支持 n8n 工作流，文档：https://docs.qq.com/doc/DV3pKbG9GS0pJS0tk

1,9301780Updated Today

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/jar-analyzer/jar-analyzer.git

Works with:

Claude

Overview

About This Skill

The Jar Analyzer is a powerful GUI tool designed for analyzing JAR files, providing developers with the ability to search for method call relationships and analyze them using DFS algorithms. This skill enables users to simulate JVM taint analysis, perform string searches, and analyze Java Web component entry points. With support for MCP calls and integration into n8n workflows, Jar Analyzer enhances workflow automation and improves the efficiency of JAR file management. One of the key benefits of using Jar Analyzer is the significant time savings it offers. Developers can quickly identify where a specific method is defined across multiple JAR files, allowing for faster debugging and vulnerability detection. For instance, it can analyze method calls to pinpoint potential security risks, such as the misuse of Runtime.exec, and visualize control flow graphs for deeper insights into method execution paths. By integrating JAR analysis into existing CI/CD workflows, teams can automate security checks, thus streamlining their development processes. This skill is particularly beneficial for developers, product managers, and AI practitioners who are involved in software security and quality assurance. By leveraging Jar Analyzer, these professionals can ensure that their applications are not only functional but also secure from potential vulnerabilities. The intermediate complexity of the skill makes it accessible to those with a foundational understanding of Java and JAR file structures, while its implementation time of just 30 minutes means that teams can quickly adopt it into their workflows. In an AI-first environment, the Jar Analyzer fits seamlessly into automated workflows, enabling teams to maintain high standards of code quality and security. As AI technology continues to evolve, skills like Jar Analyzer are essential for keeping pace with the demands of modern software development. With its practical applications and robust features, it stands out as a valuable tool for any organization looking to enhance their JAR file analysis capabilities.

Use Cases

Quickly identify where a specific method is defined across multiple JAR files.

Analyze method calls to detect potential vulnerabilities like the use of Runtime.exec.

Perform string searches to locate sensitive information leaks within JAR files.

Visualize control flow graphs for deeper insights into method execution paths.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/jar-analyzer/jar-analyzer

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the JAR file located at [JAR_FILE_PATH]. Perform a method call relationship search focusing on the method [METHOD_NAME]. Provide a detailed DFS analysis of the method call chain, including any potential taint analysis results. Additionally, identify any relevant Java Web components and suggest improvements based on your findings.

Example Output

Upon analyzing the JAR file located at '/path/to/example.jar', the method 'processOrder' was identified as a key entry point. The method call relationship reveals that 'processOrder' calls 'validateOrder', which in turn calls 'calculateTotal'. The DFS analysis indicates that 'validateOrder' is potentially vulnerable to tainting from user input due to a lack of sanitization. Furthermore, the Java Web component 'OrderController' is responsible for handling HTTP requests related to order processing. To mitigate risks, it is recommended to implement input validation and sanitization in 'validateOrder'. Additionally, consider refactoring the method to reduce complexity and enhance maintainability. The CFG analysis shows that there are multiple exit points in 'processOrder', which could lead to unexpected behavior if not handled properly. Overall, the analysis suggests a need for code refactoring and enhanced security measures.