Re-evaluate AI memory freshness for code patterns with project scoping.
npx skills add https://github.com/Hidden-History/ai-memory --skill aim-refresh
Please help me install this Agent Skill. Skill: aim-refresh Source: https://github.com/Hidden-History/ai-memory/tree/main/.claude/skills/aim-refresh Command: npx skills add https://github.com/Hidden-History/ai-memory --skill aim-refresh
This Skill addresses the issue of stale or outdated information within an AI's memory, ensuring that the agent operates with the most relevant and current context.
Run /aim-refresh to manually re-evaluate the freshness of all code-pattern memories.
High-intent search queries and answers about installing and using this skill.
To trigger a manual re-evaluation of institutional memory, simply run the /aim-refresh command to force a freshness scan across all stored code patterns. This updates the agent's context without requiring a full system reset.
An AI agent uses outdated architectural decisions when its institutional memory lacks recent context synchronization. Running a manual memory freshness re-evaluation forces the system to scan and update its stored code patterns.
Yes, you can scope a memory freshness scan to a specific project to target updates for particular code patterns. This optional filtering ensures you only re-evaluate the institutional memory relevant to your active work.
Yes, the memory freshness pipeline integrates directly with GitHub history synchronization to evaluate code pattern relevance. This integration ensures the AI's institutional memory accurately reflects recent repository commits and architectural shifts.
You should manually re-evaluate AI institutional memory when your agent begins acting on old architectural decisions or outdated code patterns. Forcing a freshness scan ensures the agent retrieves the most current project context.
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