“`html
How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions and AI Context
In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with PageRank and community detection, check Git intelligence, run dead-code detection, capture architectural decisions, generate a CLAUDE.md file, and interact with Repowise’s MCP-style tools through the CLI. Finally, we visualize the most important nodes in the repository graph to better understand the structure, influence, dependencies, and maintenance priorities of different files or modules.
Key Takeaways
- We use Repowise to build intelligence layers for a specific Python project.
- We perform various analyses including graph analysis and dead-code detection using Repowise tools.
- The process involves initializing the repository, running various commands, and visualizing important nodes in the graph.
“`
Note: The `styles.css` file is referenced but not provided here due to character limit. It should contain any necessary styles for this HTML document.
Originally published at marktechpost.com. Curated by AI Maestro.
Stay ahead of AI. Get the most important stories delivered to your inbox — no spam, no noise.
