three-layer-memory

three-layer-memory is a skill for Claude Code, Codex from mlopscommunity/Coding-Agents-Conference-skills. It costs 37 tokens per session (2,267 once invoked), scanned A, original, Apache-2.0.

A three-level memory setup for AI assistants: shared memory across tools, project context stored with the code, and a separate library for long research documents.

In plain words
What is it for?
Use it to preserve decisions and preferences, share context between applications, and organize research material for later reference.
Why use it?
Putting each kind of knowledge in the right place reduces clutter and helps the assistant recall relevant information across sessions and projects.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions Claude Code.

Good fit Use it to preserve decisions and preferences, share context between applications, and organize research material for later reference.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory
Install

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.

Any agent
npx skills add mlopscommunity/Coding-Agents-Conference-skills --skill three-layer-memory
Clone the repo
git clone --depth 1 https://github.com/mlopscommunity/Coding-Agents-Conference-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for three-layer-memory

README.md
[![agentmods](https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory/github.svg)](https://agentmods.dev/skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory)
Your own site
<a href="https://agentmods.dev/skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory"><img src="https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/three-layer-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.

agentmods 80×15 button for three-layer-memory

Your own site · 80×15
<a href="https://agentmods.dev/skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory"><img src="https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/three-layer-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,267 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.02267
Opus 5.5 $0.00015 $0.00907
Sonnet 5.5 $0.00007 $0.00453
Haiku 4.5 $0.00004 $0.00227

Measured 2mo ago against content hash 2d10b649a658, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

three-layer-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 2mo 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.

skills/three-layer-memory/SKILL.md · 220 lines

How it starts

The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Three-Layer Memory Architecture

Overview

A tiered memory system that gives Claude agents persistent knowledge across sessions by storing different types of information in the right place. Layer 1 (Memory MCP) holds cross-application global memory shared between Claude Desktop and Claude Code. Layer 2 (auto-memory) holds per-repository project context that travels with the codebase. Layer 3 (external markdown editor) serves as a research library for long-form artifacts the agent can reference but does not own.

Core principle: Different types of knowledge belong in different memory tiers. Putting everything in one place creates noise; splitting it correctly means the agent always has the right context without being overwhelmed.

Dependency: Layer 1 requires the Memory MCP server. Layer 3 requires Joplin, Obsidian, or any markdown-based knowledge tool accessible via file path or MCP.

When to Use

  • When starting a new project and configuring how the agent should remember things
  • When you notice the agent forgetting decisions, preferences, or context between sessions
  • When you work across multiple tools (Claude Desktop + Claude Code) and need shared state
  • When you have research artifacts (design docs, API references, vendor comparisons) that the agent should consult

When NOT to Use

  • Single-session throwaway tasks where persistence has no value
  • Projects where you are the only consumer of the context (just use your own notes)
  • As a replacement for proper documentation -- memory tiers supplement docs, they do not replace them

Common Mistakes

Mistake Why it's wrong
Storing everything in CLAUDE.md CLAUDE.md is for instructions, not knowledge. It tells the agent how to behave, not what it knows. Dumping facts there makes it bloated and slow to parse.
Putting project-specific decisions in global Memory MCP Global memory pollutes other projects. A database schema decision for Project A is noise when working on Project B. Use Layer 2 per-repo auto-memory instead.
Skipping CLAUDE.md routing instructions Without explicit instructions telling the agent which tier to use, it will default to whatever is easiest -- usually forgetting entirely. The routing config is what makes the system work.
Using Layer 3 for things that change frequently The external research library is for stable reference material. If the content changes every sprint, it belongs in Layer 2 auto-memory where it is versioned with the repo.
Treating memory as write-only Memory that is never pruned becomes stale and misleading. Review and clean each layer periodically. Old decisions that have been reversed are worse than no memory at all.

Read the full file on GitHub · 220 lines

Changes

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.

  1. 2mo ago First seen · 220 lines · 37 tokens per session scan A 2d10b649a658

Subscribe to this mod's changes

three-layer-memory is a skill published in the GitHub repository mlopscommunity/Coding-Agents-Conference-skills (39 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 2,267 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.

Related

three-layer-memory compared with skills from other repositories

three-layer-memory next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
three-layer-memorymlopscommunity/Coding-Agents-Conference-skillsSkill397mo agoApache-2.0A37Claude Code, Codex
knowledge-ingestopenclaw/openclawSkill394ktodayMITA80Claude Code, Codex
forgetasgeirtj/system_prompts_leaksSkill71k2d agoCC0-1.0A0Claude Code, Codex
mem0-oss-to-platformmem0ai/mem0Skill69k5d agoApache-2.0A217Claude Code, Codex
Cortexdanielmiessler/LifeOSSkill20k6d agoMITA184Claude Code
memoryopensquilla/opensquillaSkill7.1k5d agoApache-2.0A44Claude Code, Codex
ha-data-storesshiwenwen/hope-agentSkill1.9ktodayMITA115Claude Code, Codex