This project is an AI development workflow orchestrator and context management framework. It provides a context-aware project knowledge base and a structured prompting system designed to guide large language models through the planning, implementation, and verification phases of software development.
The system optimizes AI coding contexts by using a collection of markdown files to track project state and architectural memory. It employs mode-based rule isolation and just-in-time context loading to reduce noise and ensure that only relevant rules and documentation are active for a given task.
The framework covers technical implementation planning and change execution, utilizing a plan-verify-reflect cycle to maintain quality. It manages project governance through a state-driven task registry, complexity-based workflow routing, and architectural design documentation to analyze design options and track system patterns.