architect

Manage AI project context by initializing a three-layer framework that organizes knowledge into Product, Project, and Task layers, ensuring consistency across development cycles. This structure separates stable references, current builds, and subagent capabilities.

The best way to bootstrap an AI project context framework is to run an initialization command that sets up directories, master context files, and IDE integrations. This automated project initialization creates the entire context structure needed for your software.

Yes, you can retrieve past decisions using context management commands designed to search the established knowledge base. This allows you to quickly query the structured context layers for specific historical development documentation.

Yes, the context management framework supports specialized system setup like pgvector. You can configure these specialized context systems alongside the standard Product, Project, and Task layers during or after project initialization.

Archive old contexts and check status using dedicated context management commands provided by the framework. These commands help maintain a clean knowledge base by moving outdated information while monitoring the current state of your project layers.

You need a three-layer context framework when managing complex AI projects that require strict knowledge organization across stable product references, active project builds, and specific subagent tasks. It ensures consistency and prevents context loss across development cycles.