swiftlens

🥈Silver

SwiftLens is a Model Context Protocol (MCP) server that provides deep, semantic-level analysis of Swift codebases to any AI models. By integrating directly with Apple's SourceKit-LSP, SwiftLens enables AI models to understand Swift code with compiler-grade accuracy.

12250Updated 1mo ago

Intermediate30min to implementautomation

Saves ~90 min per use

Quick InstallView Source

git clone https://github.com/swiftlens/swiftlens.git

Works with:

Claude

Overview

About This Skill

How to Use

["Install and configure SwiftLens MCP server following the official documentation at [SWIFTLens_GITHUB_REPO]. Ensure SourceKit-LSP is properly set up in your development environment.","Navigate to your Swift project directory and run the analysis using your preferred AI assistant (e.g., 'Analyze the Swift codebase in [PROJECT_PATH] using SwiftLens to identify performance bottlenecks').","Review the detailed analysis report generated by SwiftLens, paying special attention to the highlighted issues and recommendations.","Implement the suggested fixes in your codebase and re-run the analysis to verify improvements.","For ongoing maintenance, integrate SwiftLens into your CI/CD pipeline to automatically analyze new code changes before merging."]

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/swiftlens/swiftlens

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 Swift codebase in [PROJECT_PATH] using SwiftLens to identify [SPECIFIC_ANALYSIS_TYPE]. Focus on [KEY_AREAS_OF_INTEREST] and highlight any potential issues, performance bottlenecks, or architectural concerns. Provide actionable recommendations for [IMPROVEMENT_AREA].

Example Output

After analyzing the Swift codebase for 'AcmeWeatherApp' located at `/projects/AcmeWeatherApp`, SwiftLens identified several critical issues and optimization opportunities in the network layer and data persistence modules. The analysis revealed that the `WeatherAPIService` class contains a memory leak in its `fetchCurrentWeather` method due to improper handling of the network response callback. The leak occurs when the API response is received but the completion handler is not properly released, causing a 15% increase in memory usage after 100 API calls. Additionally, the CoreData stack in the `WeatherPersistenceManager` has inefficient batch processing that leads to a 200ms delay when saving 1000 weather entries. The tool also detected architectural concerns in the VIPER pattern implementation, where the `WeatherInteractor` is violating the single responsibility principle by handling both business logic and data formatting. For the network layer, SwiftLens recommends implementing a retry mechanism with exponential backoff and moving the response parsing to a dedicated worker thread. For the persistence layer, it suggests batching CoreData saves and using `NSFetchedResultsController` for more efficient data fetching. The architectural issues could be resolved by splitting the `WeatherInteractor` into separate components for business logic and data formatting.