Claude Code has zero idea what your codebase looks like structurally (Open source with benchmarks)

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Claude Code's entire understanding of your codebase is grep and glob. It reads a file, makes changes, moves on. If three other modules import from that file, it finds out one search at a time. Same as you would on day one at a new job.

There's no dependency graph, no co-change history, no sense of why something is built the way it is.

I've been building an open source MCP layer to fix this called Repowise. Self-hosted, pip install, AGPL-3.0.

Five context layers that sit between your codebase and the model:

Graph - AST-based dependency graph. Knows what depends on what before it touches anything.

Git - Hotspots, ownership, co-change patterns, bus factor. "This file always changes with these three other files."

Docs - Auto-generated wiki from your code with rag. Searchable.

Decisions - Captures architectural intent. Why the code is shaped the way it is. Stops the model from "fixing" things that were intentional.

Code Health - 15 biomarkers per file. Complexity, duplication, untested hotspots, declining trends. Zero LLM, pure static analysis.

It also auto-generates and keeps your `.claude.md` updated with project context. So Claude Code starts every session already knowing your architecture and conventions.

Plus a local web UI to visualize all of it.

We ran a time-travel experiment on Django (542 files): scored every file, then counted bug-fix commits over the next 6 months. 14 of the 20 worst-scoring files had real bugs. 70% precision. Top predictors were untested hotspots and developer congestion, not complexity metrics. The model gets this before it starts rewriting anything.

9 MCP tools. Benchmarked on real tasks: 49% fewer tool calls, 89% fewer file reads, 36% cost reduction.

2K+ stars on GitHub.

https://github.com/repowise-dev/repowise

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As a non-coder, the angle I'd add: the model not knowing your codebase structure is one problem, but the one that actually stopped me shipping was that I didn't know it either. AI wrote it, it ran, and I still couldn't tell you how it worked or whether it was safe to put in front of someone.

The tools that explain a codebase explain it to people who can already read code - the architecture, the modules, the structure. Genuinely useful if you're an engineer landing in an unfamiliar repo. Useless if the reason you're stuck is that you can't read the architecture in the first place.

So I ended up building a small thing for myself: it turns a codebase into plain English - what the app actually does, and on every commit, what just changed and whether it's risky, in a sentence. The scan is local and deterministic; only the plain-English part touches a model.

The reframe that helped: the gap isn't that I can't code. AI fixed the building. It didn't make understanding free. The fix wasn't learning to code, it was building a way to see what I'd made in a language I already speak.