# Tree Ring Memory > Use when an AI agent needs local-first project memory recall, evidence-linked lessons, privacy-safe capture, audit, redaction, or intentional forgetting. - Skill: `yangsonhung/tree-ring-memory` (Agent Skill) - Install (CLI): `npx skillmds add yangsonhung/tree-ring-memory` - Raw SKILL.md: https://api.skillmd.com/api/skills/yangsonhung/tree-ring-memory/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: yangsonhung (https://skillmd.com/u/yangsonhung) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/yangsonhung/tree-ring-memory --- # Tree Ring Memory ## Overview Use this skill to operate Tree Ring Memory as a lifecycle-aware memory layer for AI agent work. Tree Ring Memory is for durable decisions, lessons, warnings, project conventions, user preferences, and future seeds. It is not a transcript dump or a background scraper. The core idea is that agent memory should age deliberately: - fresh work can stay detailed while it is still active - older lessons should compress into stable summaries - important failures and warnings should remain visible - durable preferences and project truths should become high-confidence memory - speculative follow-ups should stay separate from confirmed facts - sensitive data should be blocked, redacted, or forgotten ## When to Use Use this skill when: - The user asks the agent to remember, recall, consolidate, redact, or forget. - A task depends on previous project decisions, preferences, or warnings. - The agent is starting or resuming work in a repository with Tree Ring Memory or a project-local `.tree-ring` directory. - A test, incident, PR, benchmark, or review produces a lesson that should help future work. - A source document such as `AGENTS.md`, DOX, or Revolve contains durable guidance that should be summarized into memory. - The agent needs to audit stored memory before a risky change. ## Do not use Do not use this skill as the primary guide for: - Short-lived scratch notes that should disappear after the task. - Raw chain of thought or hidden reasoning. - Secrets, credentials, tokens, private keys, payment details, or other sensitive values. - Saving entire conversations instead of concise lessons or decisions. - Treating unverified claims as durable project truth. - Replacing source documents, tests, issues, PRs, or release records. ## Instructions Follow the workflow below whenever Tree Ring Memory could improve continuity. For small tasks, recall narrowly and only write memory when the lesson is clearly durable. For higher-risk work, include source checks, evidence-linked capture, and a closeout review. ## Workflow 1. Recall before acting when prior context could affect the task. 2. Prefer narrow project-scoped queries over broad global recall. 3. Read source documents directly when they exist; memory does not replace `AGENTS.md`, project docs, tests, issues, PRs, or release records. 4. Store only concise lessons, decisions, warnings, and preferences that will materially improve future work. 5. Use evidence-linked capture when a lesson comes from a reviewed run, evaluation, checkpoint, incident, branch, PR, issue, or test artifact. 6. Redact, supersede, or delete stale or sensitive memory instead of preserving known-wrong context. ## Command Reference Start with local help so commands match the installed version: ```bash tree-ring --help tree-ring evidence --help tree-ring dox sync --help tree-ring revolve sync --help ``` If the project has a local Tree Ring setup, read `.tree-ring/SKILL.md` and `.tree-ring/CLI.md` before assuming a global configuration. If a command needs the project store explicitly, include the local root: ```bash tree-ring --root .tree-ring recall --query "release decisions" tree-ring --root .tree-ring evidence --help ``` Run source adapters in dry-run mode before writing imported summaries: ```bash tree-ring dox sync --source-root . --dry-run tree-ring revolve sync --source-root revolve --dry-run tree-ring integrations scan --source-root . ``` Only write summaries that are concise, useful, source-linked, and privacy-safe. ## Ring Model Use the ring metaphor to decide retention strength: - `cambium`: active task context - `outer`: recent decisions and lessons - `inner`: older compressed project knowledge - `heartwood`: durable high-confidence truths and preferences - `scar`: important failures, regressions, rejected approaches, and warnings - `seed`: unresolved ideas, hypotheses, and follow-ups Do not promote weak evidence into `heartwood`. Use `outer` or `seed` until the user confirms durability or the evidence is strong. ## Privacy Guardrails Never store: - secrets, credentials, tokens, private keys, or payment details - raw chain of thought - temporary scratchpad notes - unverified claims as durable truth - sensitive health, financial, legal, or personal identifier details without explicit user instruction - copyrighted source text beyond short allowed excerpts When useful memory contains sensitive material, keep only a redacted operational summary with enough context to avoid repeating the same mistake. ## Closeout Checklist Before ending meaningful work, ask: - What did we decide? - What did we learn? - What should future agents avoid repeating? - Did the user state a durable preference? - Is there a future seed worth revisiting? - Is any memory wrong, stale, private, or better left unstored? Only remember answers that are durable, useful, source-grounded, and safe.