memory

name	memory
description	Use ruLake as the agent's working memory. Auto-invoke /rulake-memory:memory-recall when the agent needs to look up something it might already know; auto-invoke /rulake-memory:memory-remember after the agent learns or decides something worth pinning. Closes the self-learning loop without requiring the agent author to wire it explicitly.

Use ruLake as Working Memory

This skill turns ruLake into the agent's working memory transparently. Without an explicit slash command, the agent should call memory-recall before answering questions about facts it might already know, and call memory-remember after learning new facts or making decisions.

When to recall

Before answering, ask:

• "Did the user already tell me this?" → /rulake-memory:memory-recall
• "Did I already decide this?" → /rulake-memory:memory-recall
• "Did I already do this lookup?" → /rulake-memory:memory-recall (cached tool results)
• "What's relevant context from past sessions?" → /rulake-memory:memory-recall with the topic

If memory-recall returns a hit with decision_trace.witness.match: true, use the cached answer. Skip the redundant work. Cite the witness in the response so the user knows it came from memory.

When to remember

After completing work, ask:

• "Did I make a decision the user might re-ask about?" → /rulake-memory:memory-remember
• "Did the user share a preference / fact / constraint?" → /rulake-memory:memory-remember key=user-prefs/...
• "Did I run an expensive tool call whose result is reusable?" → /rulake-memory:memory-remember key=tool-results/<call-hash>
• "Did I learn something general about this domain?" → /rulake-memory:memory-remember memory_class=semantic

Pick the memory_class honestly:

• working — current task scratchpad (TTL-able)
• episodic — "this happened at time T in session S"
• semantic — general facts the agent should know forever
• procedural — "how to do X" — recipes / patterns

When to refuse to remember

Don't pin things that change frequently — the witness will refuse on next recall and the cache churn defeats the purpose. Use Eventual{ttl_ms} consistency or skip remembering for:

• Live prices / market data
• Anything timestamped now()
• User questions that have user-specific context (remember the answer, not the question)

Self-learning loop

Every recall feeds /memory-status. Every refusal feeds /memory-tune. Every drift feeds /memory-replay. The agent doesn't need to invoke those manually — the operator can wire them as /loop-driven workers, but the recall/remember pair is enough to make ruLake function as the agent's memory.

The whole point: memory that gets faster the more it's used, and refuses to make things up when it can't be sure.