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/etanhey/brainlayer/agents-mdgit clone --depth 1 https://github.com/EtanHey/brainlayerWhat 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 | $0.04406 | $0.04406 |
| Opus 5 | $0.02203 | $0.02203 |
| Sonnet 5 | $0.00881 | $0.00881 |
| Haiku 4.5 | $0.00441 | $0.00441 |
Grade A, and why
brainlayer 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 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.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BrainLayer — a letter from Etan
Ratified row-by-row by voice, 2026-08-07. Record:
docs.local/GRILL-RATIFIED-2026-08-06.md.
BrainLayer is the fleet's memory — both sides of it. My prompts, intent, and corrections on one side; what you all tried, what worked, what eroded and why on the other — the fleet's accumulated experience, so agents learn from each other's experience instead of re-deriving what the fleet already paid to learn. More than storage: it should give agents the understanding of WHY to go one way and not the other.
Everything traces to its origin. From any memory you can walk back to the conversation it came from. Prefer pointers to the source of truth over hoarding copies.
Every indexed source carries its class. CLI coding agents (Claude Code, Codex, antigravity),
desktop apps (Claude Desktop, Gemini, ChatGPT), and subagents — normal subagents versus
brain-workers. The class is labeled at ingest and decides default-search visibility and importance.
Desktop apps: indexed, hidden from default search, opt-in only — and the opt-in switch stays
unadvertised so benchmark lanes can't trip it. Normal subagents: indexed, labeled, expandable as a
subagent thread. Brain-workers: always saved, never in default searches, never demoting other
results — [OPEN] whether they index at all, and they should ship with BrainLayer itself. All
transcripts get saved, so every memory can trace back. A new source enters only by deliberate
wiring; nothing is auto-detected.
Store discipline. Verify, then store. Do the thing, then store it — never store-before-work as
ritual. When something you stored turns out wrong, UPDATE it, don't store again beside it. If it's
my intent you corrected, come back and ask me if you got it right — and make sure your question
actually reached me: a question buried under recon output was never asked. Standing rules carry
their date and expiry — a stale truth must not masquerade as current. [NOT A FEATURE YET — this is where I'm driving this]
Eventually BrainLayer should be able to sleep like a human brain does: during the night or
hibernation periods it runs local or large-context models to figure out what the current truths
still are, and learns. That's what a brain does when it sleeps.
Never silently degrade. Never auto-delete personal data. Transcripts move to the archive only after they're embedded, and only if the usage readers still see everything. Test data changes against a copy of the real database before merging. Merged is not deployed — verify the thing that executes. Never ship my database inside a package.
This letter is for agents building BrainLayer. The rules for agents USING it live in the tool descriptions — keep those true.
— Etan
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.
- 2d ago First seen · 266 lines · 4,406 tokens per session scan A 3ee2fc286d78
brainlayer AGENTS.md is an instructions file published in the GitHub repository EtanHey/brainlayer (8 stars, last pushed 8d ago), licensed Apache-2.0. It adds 4,406 tokens to every session, about $0.0220 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.
Other instructions, from other repositories
remnic AGENTS.md
Instructions for joshuaswarren/remnic, covering remnic - agent guide, architecture boundaries (non-negotiable), upstream references, adapter implementation rules and openclaw compatibility window.
sibyl CLAUDE.md
Instructions for hyperb1iss/sibyl, covering sibyl development guide, project overview, sibyl integration, always use skills and research → do → reflect cycle.
Dragon-Brain CLAUDE.md
Instructions for iikarus/Dragon-Brain, covering dragon brain — claude.md, the harness, audit remediation (april–may 2026, complete 2026-05-09), the lie this audit closed and the contract that matters now.
vellis AGENTS.md
Instructions for volantlabs/vellis, covering agents.md, repository purpose, startup checks, installing vellis for a user and model-first component workflow.
TideMind AGENTS.md
Instructions for SawyerHan-AI/TideMind, covering externabrain 项目规则, 开源/闭源架构, 开发约定, backlog and 安装与构建.
pos CLAUDE.md
Instructions for xcota/pos, covering personal os, rules, memory, budget and hard rules.