Search and retrieve memories by keywords and optional type filters.
npx skills add https://github.com/openakita/openakita --skill search-memory-openakita
Please help me install this Agent Skill. Skill: search-memory Source: https://github.com/openakita/openakita/tree/main/skills/system/search-memory Command: npx skills add https://github.com/openakita/openakita --skill search-memory-openakita
This Skill helps you quickly retrieve specific information from your past interactions, user preferences, or learned knowledge, preventing the need to re-explain or re-discover information.
Search for memories related to Python code style preferences.
High-intent search queries and answers about installing and using this skill.
To retrieve past user preferences from a knowledge base, search historical AI memory using specific keywords. This Skill recalls established rules and preferences, preventing the need to re-explain context in new interactions.
Yes, you can filter memory retrieval by specific types like error logs or learned skills. The Skill applies optional type filters alongside keyword search to narrow down historical data.
The best way to search past conversations for a coding style is to query the AI memory storage with relevant keywords. This instantly recalls the specific coding preference previously discussed.
Recalling established rules from memory works by searching a storage mechanism with targeted keywords. The retrieval process accesses historical data and rules to apply them to current tasks.
Yes, you need an existing memory storage and retrieval mechanism to search historical AI interactions. The Skill queries this data source to access past conversations, facts, and error logs.
A knowledge base search might fail to find previously discussed facts if the memory storage mechanism is empty or the search keywords do not match the stored historical data.
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