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 instructions/neoneye/agent-memory-atlas/agents-mdgit clone --depth 1 https://github.com/neoneye/agent-memory-atlasWrote 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/instructions/neoneye/agent-memory-atlas/agents-md)<a href="https://agentmods.dev/instructions/neoneye/agent-memory-atlas/agents-md"><img src="https://agentmods.dev/badge/instructions/neoneye/agent-memory-atlas/agents-md.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 | $0.01758 | $0.01758 |
| Opus 5 | $0.00879 | $0.00879 |
| Sonnet 5 | $0.00352 | $0.00352 |
| Haiku 4.5 | $0.00176 | $0.00176 |
Grade A, and why
agent-memory-atlas AGENTS.md 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 yesterday.
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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working in this repository
A code-grounded field guide to agent memory systems: 357 reports, each read at a pinned commit, plus 21 design patterns extracted from them.
There are two jobs here, and they use different parts of the repo.
Job 1 — You are designing or building memory for some other product
Start with the use-the-atlas skill (.agents/skills/use-the-atlas/). It is
this section as a workflow: read the target repository, pick a profile, write a
build brief, stop for approval, implement in an order where each stage stands
alone, then run the tests by id. The rest of this section is what it is built on.
Do not read the reports. There are hundreds and reading widely is how an agent ends up recommending the most interesting mechanism instead of the smallest sufficient one. Read five things, in this order.
-
content/patterns/index.md, the section How to use the library. It is a list of failure modes, each pointing at the pattern that closes it. Start from the failure the product cannot tolerate, not from the mechanism that sounds most rigorous. -
The Stacks, by what you are building table on the same page. Five rows — single-user tool, multi-tenant, companion/roleplay, autonomous actor, and memory that must be correctable and defensible — each naming the failure that actually hurts for that shape. Pick the row, then read the What you can defer paragraph under it, which is the part that keeps the build small.
-
The pattern pages you selected. Each carries
Cost to adopt,Tradeoffs,Implementation checklist,Tests to require, andSeen in the atlas. The last one names the systems worth reading — that is your entry point into the corpus, and the only one you need. -
content/overview.md§8 What I Would Build and §10 Practical Checklist. §8 gives a build order in which each stage works on its own; vector search and model-based extraction come last, deliberately.
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.
- yesterday Changed · +4 lines · +69 tokens per session 13f861202eaa
- 5d ago First seen · 134 lines · 1,689 tokens per session scan A 1dee1ab96fb6
agent-memory-atlas AGENTS.md is an instructions file published in the GitHub repository neoneye/agent-memory-atlas (61 stars, last pushed today), licensed MIT. It adds 1,758 tokens to every session, about $0.0088 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.
Other instructions, from other repositories
honcho CLAUDE.md
Claude Code instructions for plastic-labs/honcho, covering claude.md, honcho overview, what is honcho?, core concepts and peer paradigm.
dense-mem AGENTS.md
AGENTS.md instructions for markhuangai/dense-mem, covering dense-mem repository guidance, project context, architecture decision records, current stack and target architecture.
zer0dex AGENTS.md
AGENTS.md instructions for hermes-labs-ai/zer0dex, covering agents.md, use it for, do not use it for, minimal commands and output shape.
Perenna AGENTS.md
Instructions for scarletkc/Perenna, covering perenna agent guidelines, start with the project contract, environment, implementation boundaries and generated and versioned artifacts.
vellis AGENTS.md
AGENTS.md instructions for volantlabs/vellis, covering agents.md, repository purpose, startup checks, installing vellis for a user and model-first component workflow.
memohood AGENTS.md
Instructions for maximskorohod/memohood, covering agents.md — установка плагина memohood, что это, способ b — через git (человек ставит вручную, без чата), безопасность and справочные факты (для сверки при установке).