Yesterday a quiet little CLI crossed 13,000 npm installs without much fanfare. Step 2 is the part worth knowing.
The tool is called ai-setup. It crawls your project, detects your languages and frameworks, and generates config files for Claude Code, Cursor, and OpenAI Codex. Everything runs local. Your code never touches a server.
That last part matters more than it sounds. Most AI-adjacent tooling in this space phones home with your project structure, your file names, sometimes chunks of your actual code. ai-setup does none of that. It reads your repo, does its analysis on your machine, and writes the output files right there. For anyone working on client code, proprietary systems, or anything under NDA, that is a meaningful difference.
The tool supports a wide range of stacks out of the box. TypeScript, Python, Go, Rust, React, Next.js, Django, FastAPI, and more. It does not just detect the language. It reads your dependency files, your folder structure, and your existing config files to understand what patterns you are actually using. The generated CLAUDE.md it produces is not boilerplate. It reflects your specific project.
The twist
Most tools set up your configs once and leave you on your own. Stack changes, new files appear, the configs go stale. ai-setup watches for that. It keeps your configs updated as the repo evolves, so your CLAUDE.md actually reflects your current codebase, not the snapshot from six months ago.
This is where most AI coding setups quietly fall apart. You spend an hour writing a careful CLAUDE.md when you start a project. It describes your architecture, your patterns, your conventions. Then six weeks pass. You swap out your ORM. You add a new service. You reorganize the folder structure. The CLAUDE.md stays exactly the same because updating it manually is the kind of task that never makes it onto the priority list.
The result is an AI assistant working from a map of a city that no longer exists. It suggests patterns you deprecated. It misses conventions you added. It references files you deleted. The gap between what is in your config and what is actually in your repo is where AI coding assistants go from useful to confusing.
ai-setup closes that gap automatically. Run it after a big refactor and your configs are current again in seconds.
🛠️ Set it up in 3 steps
- Install it:
npm install -g @caliber-ai-org/ai-setup - Run it from your project root:
ai-setup - It detects your stack and outputs configs for whichever AI tools you use
The detection step is where most of the intelligence lives. Running ai-setup in a Next.js project with Prisma and TypeScript produces a very different output than running it in a Django REST Framework project with Celery. It reads your package.json, your requirements.txt, your pyproject.toml, your folder conventions, and your existing config files before writing anything. You are not filling out a questionnaire. You are just running one command.
If you use multiple AI coding tools in the same project, it generates all the relevant configs in one pass. One run and your Claude Code, Cursor, and Codex setups are all pointing at the same accurate picture of your repo. That kind of consistency across tools is annoying to maintain by hand and trivial with this approach. 🎯
Pro tip
Run it again after any major refactor. The configs it generates reflect what is actually in your repo, not what you think is there. That gap is where AI coding assistants go wrong most often.
A few specific moments where re-running pays off: after adding or removing a major dependency, after reorganizing your folder structure, after introducing a new service or module, and after any migration that changes how your data layer works. These are the moments where your AI assistant’s mental model of your codebase diverges most sharply from reality.
You can also add it to your onboarding docs. New developer joins the team, clones the repo, runs ai-setup before they run anything else. Their AI tools are pre-configured for your exact stack before they write a single line of code. That is a much smoother ramp than handing someone a wiki page about which AI settings to configure manually.
If you are using Git hooks, adding ai-setup as a post-merge hook keeps configs fresh automatically. Every time you pull in a big branch, the configs update without any manual step required.
🔧 Tool of the Day: ai-setup on GitHub
It is open source, early-stage, and actively looking for feedback. If something breaks or you want a feature, open an issue. The builder is listening. The tool already hit 13,000 installs with almost no marketing behind it. That kind of organic growth usually means it is solving a real problem. Worth five minutes to try on your next project. 🚀
auto-generate ai assistant configs & prompts from your codebase – feedback wanted
by u/Substantial-Cost-429 in PromptEngineering