Search and manage AI memory across logs, SOPs, and traces.
npx skills add https://github.com/FatBy/DD-OS --skill memory-system-fatby
Please help me install this Agent Skill. Skill: memory-system Source: https://github.com/FatBy/DD-OS/tree/main/skills/memory-system Command: npx skills add https://github.com/FatBy/DD-OS --skill memory-system-fatby
This Skill provides a unified system for searching and managing all forms of AI memory, ensuring that important information is easily retrievable and organized.
search_memory with keywords like "last week's meeting decisions" to find the relevant entry across all memory sources.Search your memory for entries related to 'project planning' from the last 3 days.
High-intent search queries and answers about installing and using this skill.
To search AI memory logs and persistent knowledge bases, use a unified search system that queries daily logs, persistent memory, SOPs, and execution traces simultaneously. This enables efficient recall of past information using keyword-based retrieval.
Managing persistent memory for an AI assistant involves using a system that supports creating, updating, deleting, and tagging memory entries. This organized storage ensures critical data points remain easily retrievable across daily logs and execution traces.
Yes, you need Python to set up this unified search system for AI memory management. The system requires Python scripts to execute unified search queries and perform memory CRUD operations across daily logs and persistent knowledge bases.
You can recall specific decisions from past execution traces and daily logs by using the memory search functionality. By querying with relevant keywords, the system retrieves matching entries from your historical AI memory records.
Unified memory management handles different AI memory formats by aggregating daily logs, persistent memory, SOPs, and execution traces into a searchable interface. This ensures critical data points across various formats are organized and easily retrievable.
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