codegraph-rust

🥈Silver

CodeGraph transforms codebases into a semantically searchable knowledge graph, enabling AI agents to reason about code. It benefits developers and operations teams by improving code search and analysis. The tool connects to semantic search engines and AI coding agents, enhancing codebase understanding and agentic code analysis.

149170Updated Today

Intermediate30min to implementautomation

Saves ~240 min per use

Quick InstallView Source

git clone https://github.com/Jakedismo/codegraph-rust.git

Works with:

Claude

Overview

About This Skill

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/Jakedismo/codegraph-rust

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

Generate a code graph for the Rust project located at [PROJECT_PATH]. Use the codegraph-rust tool to analyze the codebase and identify key dependencies, functions, and data structures. Provide a visual representation of the code structure and highlight any potential performance bottlenecks or architectural issues. Additionally, suggest optimizations based on the analysis.

Example Output

After analyzing the Rust project located at /path/to/project, the codegraph-rust tool generated a detailed code graph. The graph revealed that the project has a modular structure with several key dependencies, including serde for serialization and tokio for asynchronous programming. The analysis identified a performance bottleneck in the data processing module, where a linear search algorithm was being used instead of a more efficient hash-based lookup. The tool suggested replacing the linear search with a hash map to improve performance. Additionally, the graph highlighted a potential architectural issue where the business logic was tightly coupled with the data access layer, making it difficult to test and maintain. The tool recommended refactoring the code to separate the business logic from the data access layer, which would make the code more modular and easier to test.