Claude Codex automates code review and enforcement. It orchestrates multiple AI agents for sequential reviews, with Codex as the final gatekeeper. It benefits development teams by ensuring code quality and security. It integrates with GitHub and other version control systems.
git clone https://github.com/Z-M-Huang/claude-codex.gitClaude Codex automates code review and enforcement. It orchestrates multiple AI agents for sequential reviews, with Codex as the final gatekeeper. It benefits development teams by ensuring code quality and security. It integrates with GitHub and other version control systems.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Z-M-Huang/claude-codexCopy 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.
Review the following code changes for [COMPANY]'s [PROJECT_NAME] in [PROGRAMMING_LANGUAGE]. Check for security vulnerabilities, adherence to [COMPANY]'s coding standards, and potential performance issues. Provide a detailed report with actionable recommendations. Here is the code: [CODE_SNIPPET].
# Code Review Report for [PROJECT_NAME] ## Security Vulnerabilities - **SQL Injection Risk**: The code uses string concatenation to build SQL queries. Recommend using parameterized queries instead. - **Authentication Bypass**: The `authenticate_user` function does not validate user input properly. Recommend adding input validation. ## Coding Standards Compliance - **Naming Conventions**: The function `calcTotal()` should be renamed to `calculate_total()` to follow [COMPANY]'s naming conventions. - **Code Duplication**: The `process_data` function has duplicate logic. Recommend refactoring to a helper function. ## Performance Issues - **Inefficient Loops**: The `generate_report` function uses nested loops. Recommend optimizing with list comprehensions. - **Memory Usage**: The `load_data` function loads entire datasets into memory. Recommend using generators for large datasets. ## Recommendations 1. Implement parameterized queries to prevent SQL injection. 2. Add input validation to the `authenticate_user` function. 3. Refactor duplicate code in the `process_data` function. 4. Optimize loops in the `generate_report` function. 5. Use generators for large datasets in the `load_data` function.
Agents that listen, think and act for you.
AI assistant built for thoughtful, nuanced conversation
IronCalc is a spreadsheet engine and ecosystem
Customer feedback management made simple
Enterprise workflow automation and service management platform
Automate your spreadsheet tasks with AI power