TScanner is a code quality scanner designed for AI-generated code. It helps developers and operations teams identify issues in AI-produced code, ensuring better quality and reliability. It integrates with popular development tools and workflows, such as ESLint and CLI, to streamline the code review process.
git clone https://github.com/lucasvtiradentes/tscanner.gitTScanner is a code quality scanner designed for AI-generated code. It helps developers and operations teams identify issues in AI-produced code, ensuring better quality and reliability. It integrates with popular development tools and workflows, such as ESLint and CLI, to streamline the code review process.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/lucasvtiradentes/tscannerCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the following AI-generated code for potential issues. The code is for a [COMPANY] in the [INDUSTRY] sector. The code is as follows: [CODE]. Identify any syntax errors, logical issues, security vulnerabilities, and performance bottlenecks. Provide a detailed report with recommendations for improvement.
# Code Quality Report ## Issues Identified ### Syntax Errors - Line 45: Missing semicolon at the end of the statement. - Line 120: Unmatched parenthesis in the if condition. ### Logical Issues - The function `calculateDiscount` does not handle negative input values, which could lead to incorrect discount calculations. - The loop in `processData` may run indefinitely if the `data` array is not properly initialized. ### Security Vulnerabilities - SQL injection risk in the `queryDatabase` function due to string concatenation. - Insufficient input validation in the `userLogin` function. ### Performance Bottlenecks - The `generateReport` function loads all data into memory, which could cause performance issues with large datasets. - The `sortData` function uses a simple bubble sort algorithm, which is inefficient for large datasets. ## Recommendations ### Syntax Errors - Add the missing semicolon on line 45. - Add the missing parenthesis on line 120. ### Logical Issues - Add input validation to handle negative values in the `calculateDiscount` function. - Initialize the `data` array before the loop in the `processData` function. ### Security Vulnerabilities - Use parameterized queries in the `queryDatabase` function to prevent SQL injection. - Add input validation in the `userLogin` function to ensure all fields are properly sanitized. ### Performance Bottlenecks - Implement pagination or streaming to load data in chunks in the `generateReport` function. - Replace the bubble sort algorithm with a more efficient algorithm like quicksort or mergesort in the `sortData` function.
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