There’s a flood of generic “best AI agent setups” that don’t reflect your codebase. As a builder frustrated by this, I created Caliber, an open-source CLI to automate the process of generating an AI agent setup tailored to your project. Caliber continuously scans your codebase — languages, frameworks, dependencies and file structure — and synthesises a set of skills, configuration files and recommendations for multi-agent coordination protocols (MCPs) appropriate for your stack. It writes files like CLAUDE.md, .cursor/rules/*.mdc and an AGENTS.md playbook, and suggests local MCP servers with the right capabilities. Under the hood it uses curated templates and configuration patterns contributed by the community and research. The tool runs locally and never sends your code to a server; you supply your own API keys. It also hooks into your version control so the recommendations evolve as your code changes. Caliber is MIT-licensed and I built it to make agent setups reproducible and safe for any project. I’m happy to share details and answer questions; the code and docs are on GitHub and a demo site. (links in comments)
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Originally posted by u/Substantial-Cost-429 on r/ArtificialInteligence