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 EtanHey/brainlayer --skill memorygit clone --depth 1 https://github.com/EtanHey/brainlayerWrote 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/etanhey/brainlayer/memory)<a href="https://agentmods.dev/skills/etanhey/brainlayer/memory"><img src="https://agentmods.dev/badge/skills/etanhey/brainlayer/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/etanhey/brainlayer/memory"><img src="https://agentmods.dev/badge/skills/etanhey/brainlayer/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 | $0.00028 | $0.00151 |
| Opus 5.5 | $0.00011 | $0.00060 |
| Sonnet 5.5 | $0.00006 | $0.00030 |
| Haiku 4.5 | $0.00003 | $0.00015 |
Grade A, and why
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.
What it actually says
BrainLayer Memory
Use BrainLayer when session context is incomplete or when the task depends on prior decisions.
- Run
brain_searchbefore answering architecture, project-history, preference, or "what did we decide" questions. - Run
brain_recallwhen you need the current working context, recent session state, or session-linked summaries. - Run
brain_storeafter decisions, corrections, failures, or milestones so the next Claude session can recover the why, not just the code diff.
Before answering from memory, verify with brain_search instead of assuming.
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 · 15 lines · 28 tokens per session scan A 675bb4f07000
memory is a skill published in the GitHub repository EtanHey/brainlayer (9 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 151 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 2026-08-31.
Other skills, from other repositories
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
remnic-memory-workflow
Shared memory workflow for Claude Code agents connected to Remnic — recall before acting, observe during work, remember at the end. Trigger phrases include "what do you remember about", "save this for later", "any context from last time".
remnic-recall
Search Remnic memories by natural-language query. Trigger phrases include "what do you remember about", "recall anything on", "have we discussed".
remnic-remember
Store a durable memory in Remnic so every connected agent can recall it. Trigger phrases include "remember this", "save this for later", "add a note that".
remnic-search
Run a deep full-text search across every Remnic memory. Trigger phrases include "search memories for", "find anything about", "deep search".
remnic-status
Check the health of the Remnic daemon, stores, and connected clients. Trigger phrases include "is remnic running", "check memory status", "daemon health".