brain-bootstrap

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 brain CLI. 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 log history. 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.

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Brownfield — synthesize from code, docs, and history

Gather from three sources, then synthesize (do not transcribe):

1. Existing code — directory structure, module boundaries, entry points, the tech stack and dependencies (manifest files: package.json, pyproject.toml, go.mod, Cargo.toml, …). This is your strongest evidence for architecture and stack.
2. Existing docs — README, docs/, CONTRIBUTING, design notes. Good for stated intent, goals, and naming.
3. git log — read the commit messages (git log --oneline -n 200 and spot-read full messages for the interesting ones). Mine them for the real decisions that were made over time.

Draft the six root pages

Write each with echo "<body>" | brain update-root <slug> (body on stdin). Lean on ```mermaid blocks (graph / sequenceDiagram / mindmap / gantt) to keep them visual.

• architecture — layers, modules, boundaries, a mermaid graph. Inferable from code — write it with confidence.
• stack — domain / choice / rationale table from the dependencies and how they're used. Inferable from code.
• flow — the end-to-end path of a typical request/operation, a mermaid sequenceDiagram. Inferable where the entry points and call paths are clear.
• mindmap — main feature branches from the project root, a mermaid mindmap. Inferable from the module/feature layout.
• background — why the project exists / goals / non-goals / target users. Often NOT inferable from code — see the guardrails.
• roadmap — milestones, a mermaid gantt. Usually NOT inferable from code — see the guardrails.

Capture key historical decisions as pages

For each genuine decision you can see in the history or the code (e.g. "switched from X to Y", "adopted pattern Z", a deliberate constraint), create a decision page and fill in its understanding:

brain create-page --id <kebab-id> --category decision --title "<one-line decision>" --source "git log / code"
echo "<what was decided, the alternatives, the rationale, the blast radius>" | \
  brain update-truth --id <kebab-id> --summary "captured from project history" --source "git log"

Link related pages and the relevant root area with [[page-id]].

Quality guardrails (mandatory)

• Synthesize, don't copy. Combine the three sources into a coherent picture; never paste a README section or a directory listing verbatim.
• git log → real decisions only. Distill the commits that represent actual choices or turning points. Do not produce a commit-by-commit changelog; routine "fix typo / bump dep" commits are noise.
• Infer only what the evidence supports. architecture and stack can be inferred from code and dependencies. background and roadmap usually cannot — for anything you can't ground in evidence, mark it low-confidence and ask the user to confirm, or leave it as an explicit open question. Never fabricate goals, history, or plans.
• Prefer less but accurate. Only sediment knowledge that (a) will still matter in six months and (b) is hard to reconstruct from the code itself. If the code already says it plainly, leave it in the code.

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Greenfield — interview the user

There is nothing to read, so switch to an interview. Ask open-ended questions and let the answers drive what you seed. Cover at least:

• Goal — what is this project for? What problem does it solve?
• Target users — who is it for?
• Non-goals — what is explicitly out of scope?
• Rough shape — the rough form/architecture/stack the user has in mind (may still be loose).

From the answers:

• Seed background (goal / target users / non-goals) with update-root background — this is the primary greenfield deliverable.
• If the user already has a sense of the tech choices or milestones, draft stack and/or roadmap as early sketches with update-root. Mark them as provisional. Leave the others as their templates until there's something real to say — don't invent architecture/flow/mindmap for code that doesn't exist yet.

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Wrap up (both modes)

1. Rebuild the index and review what you seeded:

   brain reindex
   brain list-pages
   

2. Report back to the user: which root pages you drafted, which decision pages you created, and anything you marked low-confidence / needs confirmation.

3. Remind the user of the standing rule: all reads and writes go through the brain CLI — never hand-edit a brain file. From here, ongoing knowledge capture flows through the brain-page and brain-ingest skills.

Write the body of root pages, compiled_truth, and timelines in the user's working language; keep technical identifiers (ids, slugs, field names, paths) verbatim.