memory-read

You can search past discussions by querying a Supabase long-term memory table, filtering records through keywords, categories, tags, or embedding vector similarity to retrieve relevant design decisions.

Yes, you can filter Supabase memory records by applying category filtering and tag-based filtering via SQL execution, allowing you to precisely retrieve past insights and stored discussions.

Yes, memory-read supports embedding vector similarity search via SQL execution, enabling you to retrieve past discussions and knowledge from the Supabase database based on semantic relevance.

You can retrieve recent records by executing a search pattern within the Supabase long-term memory table that specifically targets and fetches the most recently stored discussions and insights.

The best way is to search a long-term memory database for past design decisions related to your feature topic, retrieving stored insights to prevent redundant work and foster knowledge sharing.

Yes, you need a Supabase database configured with a long-term memory table, as this Skill relies on executing SQL queries against it to retrieve your stored discussion history and insights.