memory-bank

You can organize development logs by maintaining a time-stamped Implementation Log inside the .ai_memory directory, ensuring progress notes and feature updates are tracked chronologically with the newest entries shown first.

Tracking feature implementation progress is best handled by adding dated log entries to a centralized memory bank, which provides a structured workflow for starting features, updating progress, and checking ongoing status summaries.

To start a new feature log, copy the TEMPLATE.md file into the .ai_memory directory and create the initial dated Implementation Log entry using the template-based workflow and YAML frontmatter with name and description.

Yes, maintaining an implementation log requires YAML frontmatter containing the name and description fields, which enforces a structured metadata format for each development log entry stored in the .ai_memory directory.

Yes, you can check the status of ongoing work by viewing the dated entries in the implementation log, which provides status summaries and progress updates with the newest entries displayed first.

A centralized memory bank prevents scattered progress notes by storing all dated entries in a single .ai_memory directory, enforcing a consistent template-based structure that simplifies status checks and feature tracking.