Sage is an LLM council that oversees and reviews the actions of coding agents. It benefits developers and operations teams by ensuring code quality and consistency. Sage integrates with coding agents and can be applied in software development workflows to automate code reviews and maintain standards.
git clone https://github.com/usetig/sage.gitThe 'sage' skill is an innovative AI automation tool designed to act as an LLM council that meticulously reviews the actions of your coding agent. By leveraging advanced algorithms, sage provides real-time feedback and suggestions, enhancing the accuracy and efficiency of your coding processes. This skill is particularly beneficial for developers and AI practitioners who seek to improve their coding agents' performance and reduce errors in their workflows. One of the key benefits of implementing sage is its ability to streamline coding practices, potentially saving developers significant time in debugging and refining code. While the exact time savings are not quantified, the skill's intermediate complexity suggests that users can expect a smoother coding experience with fewer iterations required. This leads to faster project completion times and a more efficient use of resources, making it an essential asset for product managers overseeing development teams. Sage is ideally suited for developers and product managers who are looking to enhance their AI automation capabilities. Its application can be particularly valuable in environments where coding accuracy is paramount, such as in data engineering or software development projects. For example, a development team working on a complex software application can utilize sage to ensure their coding agent adheres to best practices, thereby minimizing the risk of bugs and enhancing overall software quality. With a moderate implementation time of approximately 30 minutes, sage is accessible for those with intermediate technical skills. It fits seamlessly into AI-first workflows by providing a layer of oversight that complements existing coding practices. As organizations increasingly integrate AI into their operations, tools like sage will be crucial in ensuring that AI agents operate at peak performance, ultimately driving better outcomes in workflow automation.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/usetig/sageCopy 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.
Act as Sage, an LLM council overseeing coding agents. Review the following code changes for [PROJECT_NAME] in [PROGRAMMING_LANGUAGE]. Provide feedback on quality, consistency, and potential improvements. Here is the code: [CODE_SNIPPET]
## Code Review for [PROJECT_NAME] in [PROGRAMMING_LANGUAGE] ### Overall Assessment - **Code Quality**: The code is well-structured and follows best practices for [PROGRAMMING_LANGUAGE]. - **Consistency**: The code adheres to the project's style guide and naming conventions. - **Improvements**: Minor optimizations can be made for better performance. ### Detailed Feedback 1. **Function Naming**: The function `calculateTotal()` is clear and descriptive. 2. **Error Handling**: Consider adding more specific error handling for edge cases. 3. **Performance**: The loop in `processData()` can be optimized by using list comprehension. 4. **Documentation**: Add docstrings to explain the purpose of the `validateInput()` function. ### Recommendations - Implement the suggested optimizations for better performance. - Add comprehensive error handling to make the code more robust. - Ensure all functions are well-documented for future maintainability.
Automate invoicing and financial reporting for streamlined business management.
Enhanced code review for improved workflow
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