gbrain/skills/setup/SKILL.md

Setup GBrain

Set up GBrain from scratch. Target: working brain in under 5 minutes.

Install (if not already installed)

bun add github:garrytan/gbrain

How GBrain connects

GBrain connects directly to Postgres over the wire protocol. NOT through the Supabase REST API. You need the database connection string (a postgresql:// URI), not the project URL or anon key. The password is embedded in the connection string.

Use the Shared Pooler connection string (port 6543), not the direct connection (port 5432). The direct hostname resolves to IPv6 only, which many environments can't reach. Find it: go to the project, click Get Connected next to the project URL, then Direct Connection String > Session Pooler, and copy the Shared Pooler connection string.

Do NOT ask for the Supabase anon key. GBrain doesn't use it.

Why Supabase

Supabase gives you managed Postgres + pgvector (vector search built in) for $25/mo:

• 8GB database + 100GB storage on Pro tier
• No server to manage, automatic backups, dashboard for debugging
• pgvector pre-installed, just works
• Alternative: any Postgres with pgvector extension (self-hosted, Neon, Railway, etc.)

Prerequisites

• A Supabase account (Pro tier recommended, $25/mo) OR any Postgres with pgvector
• An OpenAI API key (for semantic search embeddings, ~$4-5 for 7,500 pages)
• A git-backed markdown knowledge base (or start fresh)

Available init options

• gbrain init --supabase -- interactive wizard (prompts for connection string)
• gbrain init --url <connection_string> -- direct, no prompts
• gbrain init --non-interactive --url <connection_string> -- for scripts/agents
• gbrain doctor --json -- health check after init

There is no --local, --sqlite, or offline mode. GBrain requires Postgres + pgvector.

Phase A: Supabase Setup (recommended)

Guide the user through creating a Supabase project:

1. "Go to https://supabase.com and sign up or log in."
2. "Click 'New Project' in the top left."
   • Name: gbrain
   • Region: pick the one closest to you
   • Database password: generate a strong one and save it
3. "Wait about 2 minutes for the project to initialize."
4. "Find the connection string: go to your project, click Get Connected next to the project URL, then Direct Connection String > Session Pooler, and copy the Shared Pooler connection string (port 6543)."
5. Initialize gbrain:

   gbrain init --non-interactive --url "postgresql://postgres.[ref]:[password]@aws-0-[region].pooler.supabase.com:6543/postgres"
   

6. Verify: gbrain doctor --json

OpenClaw agent instructions: Store the Supabase access token in your persistent env as SUPABASE_ACCESS_TOKEN. gbrain doesn't store it, you need it for future gbrain doctor runs. Generate at: https://supabase.com/dashboard/account/tokens

Phase B: BYO Postgres (alternative)

If the user already has Postgres with pgvector:

1. Get the connection string from the user.
2. Run: gbrain init --non-interactive --url "<connection_string>"
3. Verify: gbrain doctor --json

If the connection fails with ECONNREFUSED and the URL contains supabase.co, the user probably pasted the direct connection (IPv6 only). Guide them to the Session pooler string instead (see Phase A step 4).

Phase C: First Import

1. Discover markdown repos. Scan the environment for git repos with markdown content.

echo "=== GBrain Environment Discovery ==="
for dir in /data/* ~/git/* ~/Documents/* 2>/dev/null; do
  if [ -d "$dir/.git" ]; then
    md_count=$(find "$dir" -name "*.md" -not -path "*/node_modules/*" -not -path "*/.git/*" 2>/dev/null | wc -l | tr -d ' ')
    if [ "$md_count" -gt 10 ]; then
      total_size=$(du -sh "$dir" 2>/dev/null | cut -f1)
      echo "  $dir ($total_size, $md_count .md files)"
    fi
  fi
done
echo "=== Discovery Complete ==="

2. Import the best candidate. For large imports (>1000 files), use nohup to survive session timeouts:

   nohup gbrain import <dir> --no-embed --workers 4 > /tmp/gbrain-import.log 2>&1 &
   

   Then check progress: tail -1 /tmp/gbrain-import.log

   For smaller imports, run directly:

   gbrain import <dir> --no-embed
   

3. Prove search works. Pick a semantic query based on what you imported:

   gbrain search "<topic from the imported data>"
   

   This is the magical moment: the user sees search finding things grep couldn't.

4. Start embeddings. Refresh stale embeddings (runs in background). Keyword search works NOW, semantic search improves as embeddings complete.

5. Offer file migration. If the repo has binary files (.raw/ directories with images, PDFs, audio):

   "You have N binary files (X GB) in your brain repo. Want to move them to cloud storage? Your git repo will drop from X GB to Y MB. All links keep working."

If no markdown repos are found, create a starter brain with a few template pages (a person page, a company page, a concept page) from docs/GBRAIN_RECOMMENDED_SCHEMA.md.

Phase D: Brain-First Lookup Protocol

Inject the brain-first lookup protocol into the project's AGENTS.md (or equivalent). This replaces grep-based knowledge lookups with structured gbrain queries.

BEFORE (grep) vs AFTER (gbrain)

Task	Before (grep)	After (gbrain)
Find a person	grep -r "Pedro" brain/	gbrain search "Pedro"
Understand a topic	grep -rl "deal" brain/ | head -5 && cat ...	gbrain query "what's the status of the deal"
Read a known page	cat brain/people/pedro.md	gbrain get people/pedro
Find connections	grep -rl "Brex" brain/ | xargs grep "Pedro"	gbrain query "Pedro Brex relationship"

Lookup sequence (MANDATORY for every entity question)

1. gbrain search "name" -- keyword match, fast, works without embeddings
2. gbrain query "what do we know about name" -- hybrid search, needs embeddings
3. gbrain get <slug> -- direct page read when you know the slug from steps 1-2
4. grep fallback -- only if gbrain returns zero results AND the file may exist outside the indexed brain

Stop at the first step that gives you what you need. Most lookups resolve at step 1.

Sync-after-write rule

After creating or updating any brain page in the repo, sync immediately so the index stays current:

gbrain sync --no-pull --no-embed

This indexes new/changed files without pulling from git or regenerating embeddings. Embeddings can be refreshed later in batch (gbrain embed --stale).

gbrain vs memory_search

Layer	What it stores	When to use
gbrain	World knowledge: people, companies, deals, meetings, concepts, media	"Who is Pedro?", "What happened at the board meeting?"
memory_search	Agent operational state: preferences, decisions, session context	"How does the user like formatting?", "What did we decide about X?"

Both should be checked. gbrain for facts about the world. memory_search for how the agent should behave.

Phase E: Load the Production Agent Guide

Read docs/GBRAIN_SKILLPACK.md. This is the reference architecture for how a production agent uses gbrain: the brain-agent loop, entity detection, enrichment pipeline, meeting ingestion, cron schedules, and the five operational disciplines.

Inject the key patterns into the agent's system context or AGENTS.md:

1. Brain-agent loop (Section 2): read before responding, write after learning
2. Entity detection (Section 3): spawn on every message, capture people/companies/ideas
3. Source attribution (Section 7): every fact needs [Source: ...]
4. Iron law back-linking (Section 15.4): every mention links back to the entity page

Tell the user: "The production agent guide is at docs/GBRAIN_SKILLPACK.md. It covers the brain-agent loop, entity detection, enrichment, meeting ingestion, and cron schedules. Read it when you're ready to go from 'search works' to 'the brain maintains itself.'"

Phase F: Health Check

Run gbrain doctor --json and report the results. Every check should be OK. If any check fails, the doctor output tells you exactly what's wrong and how to fix it.

Error Recovery

If any gbrain command fails, run gbrain doctor --json first. Report the full output. It checks connection, pgvector, RLS, schema version, and embeddings.

What You See	Why	Fix
Connection refused	Supabase project paused, IPv6, or wrong URL	Use Session pooler (port 6543), or supabase.com/dashboard > Restore
Password authentication failed	Wrong password	Project Settings > Database > Reset password
pgvector not available	Extension not enabled	Run CREATE EXTENSION vector; in SQL Editor
OpenAI key invalid	Expired or wrong key	platform.openai.com/api-keys > Create new
No pages found	Query before import	Import files into gbrain first
RLS not enabled	Security gap	Run gbrain init again (auto-enables RLS)

Phase G: Auto-Update Check (if not already configured)

If the user's install did NOT include setting up auto-update checks (e.g., they used the manual install path or an older version of the OpenClaw paste), offer it:

"Would you like daily GBrain update checks? I'll let you know when there's a new version worth upgrading to — including new skills and schema recommendations. You'll always be asked before anything is installed."

If they agree:

1. Test: gbrain check-update --json
2. Register daily cron (see GBRAIN_SKILLPACK.md Section 17)

If already configured or user declines, skip.

Schema State Tracking

After presenting the recommended directories (Phase C/E) and the user selects which ones to create, write ~/.gbrain/update-state.json recording:

• schema_version_applied: current gbrain version
• skillpack_version_applied: current gbrain version
• schema_choices.adopted: directories the user created
• schema_choices.declined: directories the user explicitly skipped
• schema_choices.custom: directories the user added that aren't in the recommended schema

This file enables future upgrades to suggest new schema additions without re-suggesting things the user already declined.

Tools Used

• gbrain init --non-interactive --url ... -- create brain
• gbrain import <dir> --no-embed [--workers N] -- import files
• gbrain search <query> -- search brain
• gbrain doctor --json -- health check
• gbrain check-update --json -- check for updates
• gbrain embed refresh -- generate embeddings