tree-ring-memorylisted

# Tree Ring Memory Use Tree Ring Memory as a lifecycle-aware memory layer, not as a transcript dump. Tree Ring Memory preserves meaningful agent learning like tree rings: - fresh work stays detailed - older learning compresses into stable rings - important warnings remain visible as scars - durable truths become heartwood - speculative future work stays as seeds - sensitive data is blocked, redacted, or kept out by default ## When To Recall Recall memory before: - starting or resuming a project - changing architecture, storage, security, privacy, or release behavior - repeating a workflow where prior failures may matter - responding to a user correction - making a decision that depends on previous preferences or constraints - editing files in a repo that has a Tree Ring Memory or `AGENTS.md` contract - closing out meaningful work and deciding what should be remembered Use narrow queries with project scope when possible. Prefer source-linked, high-confidence, non-superseded results. ## When To Remember Store a memory when the information is likely to help future work: - the user states a durable preference - the user corrects the agent - a decision is made and should survive the current session - an implementation lesson is validated by tests or production behavior - a failed approach should not be repeated - a security, privacy, release, or data-loss warning appears - a useful project convention is discovered - a future idea should be revisited later Keep memory co