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/mindmuxai/brain.md/brain-bootstrapnpx skills add mindmuxai/brain.md --skill brain-bootstrapgit clone --depth 1 https://github.com/mindmuxai/brain.mdWrote 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/mindmuxai/brain.md/brain-bootstrap)<a href="https://agentmods.dev/skills/mindmuxai/brain.md/brain-bootstrap"><img src="https://agentmods.dev/badge/skills/mindmuxai/brain.md/brain-bootstrap.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 | $0.00074 | $0.01674 |
| Opus 5 | $0.00037 | $0.00837 |
| Sonnet 5 | $0.00015 | $0.00335 |
| Haiku 4.5 | $0.00007 | $0.00167 |
Grade A, and why
brain-bootstrap 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 4d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
brain-bootstrap
brain-setup scaffolds an empty brain — six root-page templates plus an empty pages/. This skill fills it with real, durable project knowledge for the first time. It is the bridge between "a brain exists" and "the brain is worth reading".
You gather information by reasoning over the project itself — reading code, docs, and git log, or interviewing the user — but you never write the brain by hand. Every landing of knowledge is a brain CLI subcommand.
NEVER hand-edit any file under the brain directory. All reads and writes MUST go through the
brainCLI. Manual edits are unsupported and illegitimate. There is no validator and nothing at the file layer can catch a bad manual edit; correctness is guaranteed only by going through the CLI, so a hand edit silently breaks the brain's invariants.
The exact command surface (update-root / create-page / update-truth / reindex / ls …) and the page-category taxonomy live in the brain-page skill — read it before creating or modifying any page. Resolve <brain-page-bundle> to wherever that skill is installed (in the brain.md source repo, skills/brain-page/); define the shell function brain() { node <brain-page-bundle>/bin/brain.mjs "$@"; } (a function is portable across bash and zsh, unlike BRAIN="node …"; $BRAIN …, which only word-splits in bash) and run everything from the project root.
Step 0 — Pick the mode
Inspect the project to decide which path you are on:
- Brownfield — there is substantial source code, and/or real
git loghistory. There is something to read; go to Brownfield. - Greenfield — a near-empty repo: no meaningful source, little or no history. There is nothing to read; go to Greenfield.
When it is genuinely mixed (a little code, a little history), prefer Brownfield for whatever can be inferred, and fall back to interview questions for the parts the code can't tell you.
Brownfield — synthesize from code, docs, and history
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.
- 4d ago First seen · 96 lines · 74 tokens per session scan A d69a92c85499
brain-bootstrap is a skill published in the GitHub repository mindmuxai/brain.md (527 stars, last pushed 13d ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,674 once invoked, about $0.0004 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 skills, from other repositories
c-03-repo-bootstrap
Bootstrap onboarding for undocumented repos or existing memory slices. Builds root overviews, route-local overview pillars, evidence packs, file cards, onboarding waves, deleted-slice cleanup, curator reviews, and handoff while keeping the orchestrator thin.
c-12-closeout
Close out approved Agents Remember edits by preserving approval authority, mandatory strict code quality before code commit, missing-onboarding checks, external-memory refresh, memory quality, ledger alignment, and no automatic push.
c-02-memory-quality-control
Control Agents Remember memory quality with task-start drift checks, pre-code-commit missing-onboarding checks, and closeout memory quality gates.
c-13-install-and-onboard
After the harness starter package and MCP server are wired and the harness has restarted, run/verify runtimeinstall, set up the memory repo, bootstrap onboarding when scaffolding new, and configure providers to index the code and memory.
c-00-initialize-memory-repo
Initialize or repair the Agents Remember memory root for a target repository. Defaults to repo-local internal memory; creates an external memory repo only when the developer explicitly asks for external memory.
c-04-retrieval-strategy-router
Choose retrieval strategies across memory substrates: semantics for known concepts with unknown structure, relationships for known anchors with unknown connections, and intent for hidden contracts and code truths.