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 agentmods add skills/mlopscommunity/coding-agents-conference-skills/three-layer-memorynpx skills add mlopscommunity/Coding-Agents-Conference-skills --skill three-layer-memorygit clone --depth 1 https://github.com/mlopscommunity/Coding-Agents-Conference-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/mlopscommunity/coding-agents-conference-skills/three-layer-memory)<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="Measured on agentmods" 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 | $0.00037 | $0.02267 |
| Opus 5 | $0.00018 | $0.01133 |
| Sonnet 5 | $0.00007 | $0.00453 |
| Haiku 4.5 | $0.00004 | $0.00227 |
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 5d 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 — 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. |
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.
- 5d ago First seen · 220 lines · 37 tokens per session scan A 2d10b649a658
three-layer-memory is a skill published in the GitHub repository mlopscommunity/Coding-Agents-Conference-skills (37 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 2,267 once invoked, about $0.0002 per session on Opus 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.
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