stack-detection

Preventing AI code hallucinations requires detecting the project's technology stack before code generation. This skill analyzes package.json and configuration files to identify frontend, backend, database, and ORM tools, ensuring the AI uses correct, technology-specific patterns.

Detecting a project's technology stack involves analyzing package.json, configuration files, and code patterns. This process identifies frontend frameworks, backend frameworks, databases, state management, styling, and testing tools to document the full stack.

AI agents can automatically identify frontend and backend frameworks by running a stack detection checklist. This analyzes your project's configuration files and code patterns to ensure accurate technology-specific code generation.

The best way to ensure an AI assistant uses correct ORM and database patterns is to run stack detection first. It identifies specific databases and ORMs like Prisma, loading the appropriate patterns to prevent incorrect code generation.

Stack detection works for identifying build tools and validation libraries by analyzing code patterns and configuration files. It comprehensively maps out testing tools, validation libraries, state management, and styling solutions.

Your AI assistant generates code with the wrong state management or styling libraries due to unrecognized project stacks. Running stack detection first identifies these specific technologies, preventing hallucinations and ensuring accurate code generation.