Early on it's magic. You describe what you want, the AI writes it, everything works. But as your project grows, you start spending more prompts giving the AI context than actually building. "No, that function is used in three other places." "No, we did it that way on purpose because of X." "Check the other file first."
It reads files one at a time with grep. Doesn't know what's connected to what and doesn't know the history of why things are the way they are.
I built an open source tool called repowise that gives the AI that map. It's an MCP layer (works with Claude Code, Cursor, Codex, anything MCP-compatible). You run `pip install repowise && repowise init` and it indexes your entire project into five layers the model can query before it starts writing code:
Graph - Knows what depends on what. Before it touches a file, it knows what's connected.
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Git - Knows which files always change together. Even when there's no import between them.
Docs - Auto-generated wiki of your whole codebase. Searchable.
Decisions - Knows why your code is shaped the way it is. That weird workaround you asked the AI to write last week? It won't "fix" it next week.
Code Health - 15 Biomarkers that scores every file 1-10. Flags the messy ones before the AI makes them worse.
We ran a time-travel experiment on Django (542 files): scored every file with the Code Health layer, then counted bug-fix commits over the next 6 months. 14 of the 20 worst-scoring files had real bugs. Top predictors were untested hotspots and developer congestion, not complexity metrics
It also auto-generates and keeps your `.claude.md` (or rules files) updated so the AI starts every session already knowing your project structure.
Benchmarked: 49% fewer tool calls, 89% fewer file reads, 36% cost reduction.
Plus a local web UI where you can see your whole project visually. Dependencies, hotspots, dead code, all of it.
2K+ stars. Open source. Self-hosted. Free.





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