Retrieves past context from the memory layer for proactive decision-making.
npx skills add https://github.com/AbyssCN/xihe --skill recall-abysscn
Please help me install this Agent Skill. Skill: recall Source: https://github.com/AbyssCN/xihe/tree/main/skills/recall Command: npx skills add https://github.com/AbyssCN/xihe --skill recall-abysscn
This Skill enables proactive recall of past context, helping agents make informed decisions and solve problems more effectively without waiting for a hook to fire.
/recall to proactively search for past decisions and rationale that can inform the current process./recall <topic>
High-intent search queries and answers about installing and using this skill.
Proactive context retrieval uses the memory layer to let agents query past data and rationale directly, filling blind spots left by passive recall. This ensures critical historical information informs current decisions without waiting for triggers.
Active context recall is querying the memory store directly to fetch historical rationale. You need it when writing plans or specs to proactively search for past decisions that inform the current process and avoid overlooking critical information.
Memory layer interaction queries the memory store directly to retrieve historical data and reasoning. It enables cross-module reuse by allowing agents to access consolidated context from past modules during current problem-solving.
Yes, you need the Xihe system to manage and consolidate context effectively. It provides the memory layer required for this Skill to retrieve historical data, review rationale, and learn historical lessons.
The best way to query past decisions is using active context recall to search the memory layer directly. This approach retrieves historical rationale proactively, optimizing methodology queries and historical lesson learning.
Passive memory recall relies on triggered hooks, missing unprompted historical context. Active recall solves this by letting agents query the memory store directly, ensuring no critical information is overlooked during decision-making.
breakingcircuits1337Implement priority-based retrieval with access tracking, recency scoring, and importance weighting.
akshatdaltonStructure open-ended problem exploration and capture decisions in a vault.
humanerd-drewSynchronize trend analysis outputs to an AI agent's long-term memory.
sixscripts-aiExplain agent memory architectures for short-term context, long-term storage, and retrieval.