legion:workflow-common-memory

To keep AI workflow memory project-scoped, you need a memory manager that isolates memories within a single project to prevent cross-project leakage. This skill enforces project scoping so memories stay contained and do not affect other projects.

The best way to handle missing memory files is to use a non-blocking memory manager that allows commands to run anyway. This approach enforces non-blocking behavior so workflows execute successfully even when memory files are absent.

Yes, you can apply additive guidance to AI workflows without overriding mandatory role constraints. A project-scoped memory manager adds hints and preferences additively, ensuring mandatory role constraints remain intact and unaltered.

To standardize memory handling across multiple AI commands, apply project-scoped memory rules that govern outcomes, preferences, and learning. This standardization resolves memory inconsistencies and ensures cross-command coherence within your project workflows.

Yes, project-scoped memory management works when memory files are missing because it operates non-blocking. Commands still run successfully without the memory files, ensuring your workflow automation remains resilient and functional during execution.