grepai enables AI agents to perform semantic search and call graphs locally, benefiting developers and operations teams. It connects to codebases and developer tools, allowing for efficient code search and analysis without relying on external services.
git clone https://github.com/yoanbernabeu/grepai.gitgrepai is an innovative automation skill designed for AI agents, particularly Claude Code, that facilitates semantic search and call graph generation for enhanced code understanding. This skill operates entirely locally, allowing developers to quickly search through large codebases and locate critical components such as authentication logic or error handling patterns using natural language queries. By integrating grepai into your workflow, you can streamline your code navigation and improve overall efficiency. One of the key benefits of grepai is its ability to save time by automating the process of locating dependencies and understanding function calls. For instance, developers can trace function calls to identify dependencies before making changes to critical code, reducing the risk of introducing bugs. Additionally, grepai can automatically keep your code index updated with a file watcher, ensuring that your searches remain accurate and relevant. This capability is particularly useful for teams managing large and complex codebases. This skill is ideal for developers, product managers, and AI practitioners who are looking to enhance their productivity and code comprehension. With a difficulty level classified as beginner, grepai can be implemented in approximately 30 minutes, making it accessible for teams of all skill levels. Its integration with AI agents like Claude Code further enhances its utility, allowing for a more intelligent and responsive development environment. Practical use cases for grepai include quickly locating authentication logic in a large codebase, searching for error handling patterns across projects, and understanding dependencies before making significant changes. By adopting grepai, teams can leverage AI automation to facilitate workflow automation, ultimately leading to improved code quality and faster project delivery. As the demand for efficient coding practices grows, incorporating grepai into your AI-first workflows can provide a competitive edge.
Quickly locate authentication logic in a large codebase by searching for 'authentication logic'.
Trace function calls to understand dependencies before making changes to critical code.
Automatically keep your code index updated with a file watcher to ensure search accuracy.
Integrate grepai with AI agents like Claude Code to enhance code understanding and reduce input tokens.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/yoanbernabeu/grepaiCopy 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.
Utilize grepai to perform a semantic search on [DATA_SOURCE] for the term '[SEARCH_TERM]'. Generate a call graph that illustrates the relationships between the top 5 relevant results. Highlight any key insights or connections that may be useful for [USE_CASE].
After conducting a semantic search on the internal documentation database for the term 'customer retention', grepai identified five key documents: 'Retention Strategies 2023', 'Customer Feedback Analysis', 'Loyalty Programs Overview', 'Churn Prediction Models', and 'Engagement Metrics'. The call graph illustrates connections between these documents, showing that 'Retention Strategies 2023' is referenced in both 'Customer Feedback Analysis' and 'Loyalty Programs Overview'. Key insights reveal that implementing loyalty programs can directly influence customer engagement metrics, suggesting a potential area for improvement in retention strategies. This information can guide the marketing team in refining their approach to customer retention.
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