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 the 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.

import os, sys, json, subprocess, textwrap, shutil, re

from pathlib import Path

TARGET = Path("/content/itsdangerous")

assert TARGET.exists(), "Run §1–§2 first to clone the target repo."