I built a CLI that scaffolds the boring parts of an AI SaaS — here's what it actually generates
Every AI SaaS project starts with the same two days of nothing-interesting: auth, a users table, an encrypted place to store provider API keys, a Prisma schema. Then you finally get to the part you actually wanted to build. I've been working on a CLI ( @chimerai/cli ) that scaffolds exactly that prefix. This post is about what it actually writes to disk — not what a landing page would claim —…
This article details a CLI tool called @chimerai/cli, which automates the creation of an AI SaaS project, handling common but unexciting elements like authentication, user management, encryption of API keys, and a basic Prisma schema. The tool prompted the user to customize features, with defaults including authentication, role-based access control, an admin dashboard, and analytics.
Users can opt for specific features like database type (PostgreSQL with Docker) or disable Docker entirely. The generated project structure includes app components, UI components, and utility libraries for various functionalities. Prisma is used for database operations, with models generated based on selected features. The CLI ensures secure handling of API keys through AES-256 encryption.
The stack leverages Next.js 14/15 App Router, API routes for backend functionality, TypeScript for type-safe user-facing code, and Python for AI orchestration using FastAPI and FastAI libraries. This approach aims to provide clear, inspectable code rather than a dependency that cannot be audited. The CLI also integrates with Next.js' authentication and provider management systems, allowing for flexible provider configuration.
Additionally, RBAC is implemented through resource-action permission strings, ensuring secure access control.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
