gstack-learn

To search project learnings, you ask the AI to surface patterns or fixes previously captured. It reviews the knowledge base and summarizes important discoveries so your team can reuse past resolutions without repeating the same debugging work.

Pruning stale learnings removes outdated or low-signal entries from the knowledge base. This keeps your project learnings accurate and fast to scan, ensuring only relevant prior fixes and patterns remain active for future sessions.

Yes, you can export project learnings to share or move them out of local session storage. This allows the team to use captured fixes, patterns, and discoveries elsewhere, preventing redundant problem-solving across different working environments.

Proactive suggestions nudge you toward relevant project learnings when your question hints at prior patterns or repeated issues. It checks the session memory knowledge base to surface evidence of past fixes, helping resolve recurring software engineering problems faster.

Yes, managing project learnings requires gstack learnings data access via a configured learnings file. This setup enables session-aware startup preamble behavior and controlled prompting gates to properly route, capture, and surface prior fixes.

You should not rely on a session memory knowledge base if the project learnings file is unconfigured or if entries have not been pruned of stale, low-signal data. Without proper pruning and data access gates, prior fixes may be outdated or inaccessible.