agentleFS
YangsonHungYangsonHung/awesome-agent-skills/skills/en/tree-ring-memory/SKILL.md

tree-ring-memory

Skill18 starsChanged 5 days agoMITView on GitHub

Use when an AI agent needs local-first project memory recall, evidence-linked lessons, privacy-safe capture, audit, redaction, or intentional forgetting.

4 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Show its rules and text

We split this skill into the rules it gives an agent

16 rules. 0 are common, 14 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 14

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to operate Tree Ring Memory as a lifecycle-aware memory layer for AI agent work.

    line 10
  2. Distinctive:

    Use this skill when:

    line 26
  3. Distinctive:

    Do not use this skill as the primary guide for:

    line 40
  4. Distinctive:

    Follow the workflow below whenever Tree Ring Memory could improve continuity.

    line 52
  5. Distinctive:

    Prefer narrow project-scoped queries over broad global recall.

    line 60
  6. Distinctive:

    Read source documents directly when they exist.

    line 61
  7. Distinctive:

    Use evidence-linked capture when a lesson comes from a reviewed run, evaluation, checkpoint, incident, branch, PR, issue, or test artifact.

    line 65
  8. Distinctive:

    Start with local help so commands match the installed version:

    line 72
  9. Very specific:

    If the project has a local Tree Ring setup, read .tree-ring/SKILL.md and .tree-ring/CLI.md before assuming a global configuration.

    line 81
  10. Distinctive:

    Run source adapters in dry-run mode before writing imported summaries:

    line 90
  11. Distinctive:

    Only write summaries that are concise, useful, source-linked, and privacy-safe.

    line 98
  12. Distinctive:

    Use the ring metaphor to decide retention strength:

    line 102
  13. Very specific:

    Do not promote weak evidence into heartwood.

    line 111
  14. Distinctive:

    When useful memory contains sensitive material, keep only a redacted operational summary with enough context to avoid repeating the same …

    line 126
  15. Distinctive:

    Before ending meaningful work, ask:

    line 131
  16. Distinctive:

    Only remember answers that are durable, useful, source-grounded, and safe.

    line 140

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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:

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:

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:

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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.