Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add YangsonHung/awesome-agent-skills --skill tree-ring-memorygit clone --depth 1 https://github.com/YangsonHung/awesome-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/tree-ring-memory)<a href="https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/tree-ring-memory"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/tree-ring-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yangsonhung/awesome-agent-skills/tree-ring-memory"><img src="https://agentmods.dev/badge/skills/yangsonhung/awesome-agent-skills/tree-ring-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01086 |
| Opus 5.5 | $0.00013 | $0.00434 |
| Sonnet 5.5 | $0.00007 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
tree-ring-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-ringdirectory. - 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 141 lines · 33 tokens per session scan A cd17650dac60
tree-ring-memory is a skill published in the GitHub repository YangsonHung/awesome-agent-skills (18 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 1,086 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
docs-context
Super Base Context — project doc loader & synchronizer. Triggers even without explicit "load docs"/"sync docs." READ MODE — load architecture/modules/coding/decision docs when work references project state: code work (write/modify/remove); design (features/APIs/schemas/architecture); review (modules, past solutions)…
run-memory
Shared step of the marketing playbooks (analyze-performance, run-paid-campaigns, research-competitors, email-lifecycle): reads the last run's record, checks whether its recommendations were done and whether they worked, and saves a short record of this run for the next one. Loaded by those playbooks; not for direct…
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain — personal notes go there, not here. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/)…
claude-reflect
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
compound-docs
Searchable Elixir/Phoenix/Ecto solution documentation system with YAML frontmatter. Builds institutional knowledge from solved problems. Use when consulting past solutions before investigating new issues.
claude-md-updater
Scans the session for durable lessons and workflows, then proposes scoped project instruction updates. Use for save this lesson or add to context.