memory

Instructions for using BrainLayer, a memory system for coding-agent work. They define when to search or recall past context and when to store decisions, corrections, failures, and milestones.

In plain words
What is it for?
Use them when a task depends on earlier architecture choices, project history, preferences, current context, or decisions that future sessions need to recover.
Why use it?
They reduce the risk of making assumptions or losing important project history between sessions. Searching before answering and saving decisions afterward keeps the agent's context grounded.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/etanhey/brainlayer/memory
Any agent
npx skills add EtanHey/brainlayer --skill memory
Clone the repo
git clone --depth 1 https://github.com/EtanHey/brainlayer

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 151 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00028 $0.00151
Opus 5 $0.00014 $0.00076
Sonnet 5 $0.00006 $0.00030
Haiku 4.5 $0.00003 $0.00015

Measured 2d ago against content hash 675bb4f07000, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

extensions/brainlayer-plugin/skills/memory/SKILL.md · 15 lines

What it actually says

BrainLayer Memory

Use BrainLayer when session context is incomplete or when the task depends on prior decisions.

  • Run brain_search before answering architecture, project-history, preference, or "what did we decide" questions.
  • Run brain_recall when you need the current working context, recent session state, or session-linked summaries.
  • Run brain_store after 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.

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. 2d ago First seen · 15 lines · 28 tokens per session scan A 675bb4f07000

Subscribe to this mod's changes

memory is a skill published in the GitHub repository EtanHey/brainlayer (8 stars, last pushed 8d ago), licensed Apache-2.0. It adds 28 tokens to every session and 151 once invoked, about $0.0001 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 2026-08-31.

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