Project Memory Tracking Skill

To preserve project context across agent sessions, load the current project memory at session start and save new features, QA results, and decisions as work progresses. This maintains structured state for services and features.

Persistent project memory is a structured data model that stores services, features, decisions, QA results, and Docker MCP metadata. It allows software agents to avoid re-analyzing code and build directly on prior work.

Store QA results and decisions by saving them to the memory model during your session. This creates an auditable history that preserves rationale to guide future work and QA audits.

Yes, you can maintain cross-session continuity by loading and updating context as agents switch between services or start new tasks. The memory model uses Docker MCP to track service inventory and implementations.

Yes, project memory tracking works without specific dependencies. The skill operates independently to store your project state, allowing agents to retain context without requiring external modules.

The best way to avoid re-analyzing code is to load the existing project memory at the start of a new agent session. This provides immediate access to prior decisions and feature implementations.