Aggregate historical agent memory files into summaries of decisions and open tasks.
npx skills add https://github.com/F0WCUS/memory-focus --skill study-f0wcus
Please help me install this Agent Skill. Skill: study Source: https://github.com/F0WCUS/memory-focus/tree/main/skills/study Command: npx skills add https://github.com/F0WCUS/memory-focus --skill study-f0wcus
This skill solves the fragmentation of agent memory by aggregating decisions, tasks, and context from previous work sessions into a single, actionable summary.
Trigger the study skill by asking the agent to catch you up on recent progress or by using the study command with an optional tag.
High-intent search queries and answers about installing and using this skill.
To aggregate agent memory files, this skill parses historical session logs to extract decisions, open loops, and cross-references, synthesizing them into a single actionable summary for immediate task context.
Recalling past work context is done by parsing memory files from the last 7 days to identify key decisions and automatically mapping cross-references to maintain a cohesive project narrative.
Yes, you can filter synthesized agent memory using tag-based filtering, allowing you to drill down into specific project areas or priority levels using tags like #important or #decision.
Yes, you need Git integration for synchronization and access to a local memory directory to facilitate the extraction of decisions and context from your session logs.
Cross-reference mapping connects related work items across different sessions by extracting and linking decisions from historical memory files, ensuring a cohesive project narrative is maintained.
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