memory

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.