/brain-init

name: brain:init
description: "Use when setting up project memory in a new folder or on the first brain:init in a project. Creates project_brain.db, registers the project, and bootstraps it from CLAUDE.local.md, memory files, docs, and emails; safe to re-run."

/brain:init --- Initialize Project Brain

Sets up a new `project_brain.db` in the current working directory and bootstraps it from existing project knowledge.

Procedure

Step 1: Check existing state

Run:

python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py info

• If **no DB exists** → proceed to Step 2 (full init)
• If **DB exists with project(s)** → skip to Step 3 (scan only). Tell user: "Brain already initialized. Running scan for new/changed files..."

Step 2: Create the database and project record

Run:

python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py init

Ask the user:

• **Project name** (e.g., "My Project A", "My Project B")
• **Slug** (short URL-safe identifier, e.g., "my-project-a", "my-project-b")
• **Description** (one-liner)

Then run:

python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py add-project "{name}" --slug {slug} --desc "{description}"

If the project has sub-scopes (like ProjectA-Phase1 and ProjectA-Phase2 under one umbrella), ask if the user wants multiple project records.

Step 3: Scan the project folder

Run the scanner to discover what's available:

python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py scan

This returns a JSON report with:

• **manifest_diff**: new/changed/unchanged file counts, whether this is the first scan
• **files_to_process**: paths of new or changed files
• **knowledge_sources**: which CLAUDE.local.md, memory files, docs, and emails were found

Show the user a summary:

FOLDER SCAN
===========
First scan:     yes/no
Files found:    NN total (NN new, NN changed, NN unchanged)

Knowledge sources detected:
  CLAUDE.local.md:  found / not found
  CLAUDE.md:        found / not found
  Memory files:     N files (list names)
  Documents:        N files in docs/
  Emails:           N files in emails/
  Reference docs:   N files

If nothing to process (all unchanged): "Everything up to date. No new knowledge to extract." → done.

Step 4: Extract knowledge from sources (Claude-driven)

Process sources in **priority order**. For each source, read the file, extract structured knowledge, and collect proposed writes. Do NOT write to the brain yet — collect everything first.

Priority 1: CLAUDE.local.md

If found, read the full file. Extract:

• **Sections like "Key Decisions"** → `decisions` (with date, decision text, decided_by if mentioned)
• **People mentioned by name** → `person` entities (with metadata like role, email, team if mentioned)
• **Systems/tools mentioned** → `system` entities (e.g., Snowflake, Postgres, Datadog)
• **Teams mentioned** → `team` entities
• **Folder structure sections** → `document` entities for key docs
• **Recent Changes entries** → `events` (with date, type, title)

Priority 2: Memory files (~/.claude/projects/.../memory/*.md)

Each memory file has frontmatter (name, description, type) and content. Read each file:

• **project type memories** → `decisions` or context to enrich existing entities
• **feedback type memories** → skip (these are Claude behavior guidance, not project knowledge)
• **reference type memories** → `system` or `document` entities with metadata

Priority 3: Document inventory

For each file in docs/, emails/, and Reference Doc/:

• Create a `document` entity with metadata: `{"path": "relative/path", "type": "doc|email|reference", "size": N}`
• Use the filename (cleaned) as the entity name
• Do NOT read the full content of every file — just register them in the inventory

Priority 4: CLAUDE.md (project-level, if exists)

Same extraction as CLAUDE.local.md but lower priority (may overlap).

Step 5: Present extraction summary

Show proposed writes:

BRAIN BOOTSTRAP SUMMARY
========================
Project: {name} ({slug})

From CLAUDE.local.md:
  Entities:    N (list: name [type])
  Decisions:   N (list: short text)
  Events:      N (list: title)

From memory files:
  Decisions:   N (list: short text)
  Entities:    N (list: name [type])

Document inventory:
  Documents:   N (list: filename [doc|email|reference])

Total proposed writes: NN

Ask: **"Write all to brain? [Y/n/adjust]"**

Step 6: Execute writes

On confirmation, write in this order using Python:

import sys
sys.path.insert(0, '$HOME/.claude/skills/brain/scripts')
from brain.schema import get_db
from brain.operations import *

1. **Entities** — use `upsert_entity` (idempotent, safe to re-run) 2. **Relationships** — use `create_relationship` (also idempotent) 3. **Decisions** — use `create_decision` (check for duplicates by matching decision text before inserting) 4. **Events** — use `create_event` (check for duplicates by matching title + date) 5. **Document entities** — use `upsert_entity` with type="document"

After all writes, sync to MemPalace and brain.json:

from brain.memory_bridge import full_sync
full_sync("project_brain.db", project_slug)

After writes complete, update the manifest:

python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py scan-update

Step 7: Report

BRAIN INITIALIZED
=================
DB:            {path}/project_brain.db
Project:       {name} ({slug})
Schema:        v1 (11 tables)

Bootstrapped from existing knowledge:
  Entities:      +N (total: N)
  Decisions:     +N (total: N)
  Events:        +N (total: N)
  Documents:     +N (total: N)
  Relationships: +N (total: N)

Manifest updated: N files tracked

Next: Run /brain-update at end of session, or /brain-rescan when files change.

Step 8: Generate knowledge articles

After completing brain writes (Step 6) and confirming the manifest is updated (Step 6 scan-update), initialize the knowledge engine for this projec