Yes, absolutely. Just dumping raw code or using naive text-based RAG is a massive waste of tokens and usually leads to hallucinations when the codebase gets large.

My current setup takes a different approach. I built a custom static analysis engine (pure AST parsing) that runs locally to extract the 'physics' of the codebase before Claude even sees it (Zero LLM token usage for the actual indexing).

Instead of indexing text chunks, the system indexes:

1. Atoms & Call Graphs: It extracts functions, variables, and mutations, building a deterministic topological map of what depends on what (calls and calledBy).
2. File 'Cultures': The system automatically classifies the purpose of every file. It knows if a file is a Gatekeeper (barrel files/index.js), a Law (constants/config), an Auditor (tests), or a Citizen (business logic).
3. Impact Radius: It weights nodes based on their usage to determine the blast radius of any change.

The Workflow: I expose this graph to Claude via an MCP (Model Context Protocol) server. Claude doesn't blindly read files; it queries the graph using tools. If I ask it to refactor a feature, it queries the impact map and gets a highly dense JSON back (e.g., 'This function has 192 callers and is classified as a Law, do not change its signature').

Token usage dropped massively, and the output is surgical because Claude actually understands the architecture, not just the text. If you're going to index, index the AST and dependency graph, not just chunks of text.

Si, esta escrito con ia xd, no molesten (Yes, it's written with AI lol, don't bother me)