cold-start

Cold Start — Day-One Brain Bootstrapping

You have a working brain. Search works. Now what?

An empty brain is a static database. A brain with your email history, calendar, contacts, conversations, and social media is a live context membrane that makes every future interaction smarter. This skill sequences the highest-leverage data sources to get you from zero to useful in one session.

Contract

• Every import phase is gated on user consent (ask-user pattern) before proceeding.
• Google/social API access goes through ClawVisor. The agent never holds raw OAuth tokens or API keys. This is a safety requirement, not a preference. ClawVisor vaults credentials, enforces task-scoped authorization, logs every API call, and requires human approval for destructive operations. If the user doesn't want ClawVisor, the only safe alternative is offline file exports (Google Takeout, Twitter archive download).
• Each phase is independently valuable — the user can stop after any phase and still have a useful brain.
• Progress is tracked in ~/.gbrain/cold-start-state.json so interrupted sessions can resume.
• Entity detection and cross-linking run on every import, not as a separate pass.

Prerequisites

• GBrain installed and initialized (gbrain doctor --json all green)
• Brain repo cloned and synced
• Agent has terminal access and can run gbrain CLI commands

The Priority Stack

Data sources ranked by information density × ease of import:

Phase 0: ClawVisor Setup (only if your agent harness integrates ClawVisor)

Harness check first. ClawVisor requires an agent host with a ClawVisor integration (for example, an OpenClaw deployment). On harnesses without one, such as Codex or Claude Code, skip this phase: the documented default for Contacts, Calendar, and Gmail is a Google Takeout export, which covers all three offline (contacts CSV, calendar ICS, Gmail mbox). Phases 2-4 below document the Takeout path first.

Safety boundary: An AI agent with raw OAuth tokens to your Gmail, Calendar, and Contacts is an uncontrolled attack surface. One prompt injection, one malicious tool call, and your entire Google account is exposed. ClawVisor eliminates this risk class entirely.

ClawVisor is a credential gateway that sits between the agent and your APIs. The agent never sees your credentials — ClawVisor injects them at request time, enforces policies, and logs everything.

What ClawVisor gives you:

• Credential vaulting — agent sees shadow tokens, never real secrets
• Task-scoped authorization — each workflow declares exactly what it needs
• Audit trail — every API call logged with metadata (who, what, when)
• Human approval gates — destructive operations (send email, modify calendar) require your explicit approval
• Multi-service — Gmail, Calendar, Contacts, Drive, GitHub, iMessage from one gateway
• Revocation — disable the agent's access in one click, no token rotation needed

Setup (15 min):

1. Sign up at app.clawvisor.com
2. Create an agent in the dashboard, copy the agent token
3. Set environment variables (in the host agent's environment — shell profile or harness config; gbrain itself has no ClawVisor config keys, these are consumed by the host's ClawVisor integration. This requires an agent host with a ClawVisor integration, such as an OpenClaw deployment. Codex and Claude Code do not consume these variables; use the offline import path instead):

   export CLAWVISOR_URL="https://app.clawvisor.com"
   export CLAWVISOR_AGENT_TOKEN="<token>"
   

4. Activate Google services (Gmail, Calendar, Contacts) in the dashboard
5. Create a standing task with expansive scope:

   "Full brain bootstrapping: read emails, calendar events, and contacts to populate knowledge base. List, read, and search across all connected accounts."

6. Save the standing task ID the same way:

   export CLAWVISOR_TASK_ID="<task_id>"
   

Critical scoping rule: Be expansive in task purposes. "Email triage" gets rejected by intent verification. "Full executive assistant email management including inbox triage, searching by any criteria, reading emails, tracking threads" works. The intent model uses the purpose to judge each request.

If the user declines ClawVisor

Do NOT fall back to direct OAuth. Instead, proceed with offline-only imports:

• Phases 2-4 (Contacts, Calendar, Gmail) — work from a Google Takeout export
• Phase 1 (markdown/Obsidian) — works without any API access
• Phase 5 (conversation exports) — works from downloaded JSON files
• Phase 6 (X/Twitter) — works from downloaded archive
• Phase 7 (file archives) — works from local files
• Phase 8 (meeting transcripts) — works from exported transcripts

Tell the user:

"No problem. We'll work from file-based sources: a Google Takeout export covers Contacts, Calendar, and Gmail. You can set up ClawVisor anytime for live sync instead of point-in-time exports."

Do NOT offer direct OAuth as an alternative. An agent holding raw Google tokens is a security liability. The skill should not teach agents to store credentials they shouldn't have.

Phase 1: Existing Markdown / Obsidian Import

The highest-leverage first import. If the user already has a notes system, this is hundreds or thousands of structured pages ready to go.

Discovery

echo "=== Markdown Repository Discovery ==="
for dir in ~/git/* ~/Documents/* ~/notes/* ~/obsidian/*; do
  if [ -d "$dir" ]; then
    md_count=$(find "$dir" -name "*.md" -not -path "*/node_modules/*" \
      -not -path "*/.git/*" -not -path "*/.obsidian/*" 2>/dev/null | wc -l | tr -d ' ')
    if [ "$md_count" -gt 5 ]; then
      total_size=$(du -sh "$dir" 2>/dev/null | cut -f1)
      echo "  $dir ($total_size, $md_count .md files)"
    fi
  fi
done

Import

# Obsidian vaults are markdown directories — import directly, then wire wikilinks
# (full flow: skills/migrate/SKILL.md)
gbrain import /path/to/vault --no-embed --workers 4
gbrain extract links --source db      # parses [[wikilinks]] natively

# For plain markdown directories
gbrain import /path/to/dir --no-embed --workers 4

# Verify
gbrain stats
gbrain search "<topic from the imported data>"

Post-import

• Run link extraction: gbrain extract links --source db
• Run timeline extraction: gbrain extract timeline --source db
• Start embeddings: gbrain embed --stale (runs in background)

Track progress:

echo '{"phase_1_complete": true, "pages_imported": N}' > ~/.gbrain/cold-start-state.json

Phase 2: Google Contacts → People Pages

Seeds the people/ directory. Every person in your contacts becomes a brain page with name, email, phone, company, and notes. This is the foundation that all other imports build on — when Gmail references "[email protected]", the brain already knows who John is.

Via Google Takeout (default on harnesses without ClawVisor)

1. Export contacts from takeout.google.com (select Contacts, CSV format), or directly from contacts.google.com via Export → Google CSV.
2. Parse the CSV: each row carries name, email(s), phone(s), organization, and notes.
3. Run each row through the processing rules below to create people/ pages.

Via ClawVisor (ClawVisor-integrated hosts only; pseudo-code)

// Fetch all contacts
const contacts = await clawvisor('google.contacts', 'list_contacts', {
  limit: 1000,
  fields: 'names,emailAddresses,phoneNumbers,organizations,biographies'
});

Processing rules

For each contact:

1. Filter out noise — skip contacts with no name, no email, or that are clearly automated (noreply@, no-reply@, support@, notifications@)
2. Check brain first — gbrain search "name" to avoid duplicates
3. Create people/ page with:
   • Name, email(s), phone(s), company, title
   • Source attribution: [Source: Google Contacts, YYYY-MM-DD]
   • Any notes from the contact as initial context
4. Link to company — if the contact has an organization, create/update the company page and link the person to it

Quality gate

After importing 5 contacts, pause and show the user a sample page. Ask:

"Here's what a contact page looks like. Want me to continue with the rest, or adjust the format first?"

Phase 3: Google Calendar (Last 90 Days)

Meeting history with attendee context. Calendar events reveal who the user meets with, how often, and in what context. Combined with contacts, this builds a rich relationship map.

Fetch events

Via Google Takeout (default on harnesses without ClawVisor): export Calendar from takeout.google.com (ICS format, one file per calendar). Parse each event (title, start/end, attendees), keep the last 90 days, and file them into the brain structure below.

Via ClawVisor (ClawVisor-integrated hosts only; pseudo-code):

// Via ClawVisor — query ALL calendar accounts
const accounts = ['[email protected]', '[email protected]'];
for (const account of accounts) {
  const events = await clawvisor(`google.calendar:${account}`, 'list_events', {
    timeMin: new Date(Date.now() - 90 * 86400000).toISOString(),
    timeMax: new Date().toISOString(),
    singleEvents: true,
    orderBy: 'startTime'
  });
}

Brain structure

Follow the three-tier calendar architecture:

brain/daily/calendar/
├── calendar-log.md              ← compiled truth (patterns, key people)
├── YYYY/
│   ├── YYYY-MM.md               ← monthly summary
│   └── YYYY-MM-DD.md            ← daily event log

Entity enrichment

For each event with attendees:

1. Look up each attendee in the brain (they should exist from Phase 2)
2. Add a timeline entry to their page: met at [event title] on [date]
3. If an attendee has no brain page and appears in 3+ events, create one
4. Link attendees who appear in the same meeting

Phase 4: Gmail (Recent Threads)

Relationship context and active threads. Email reveals organizational relationships, ongoing conversations, and communication patterns.

On harnesses without a ClawVisor integration, the source is the Gmail mbox file from a Google Takeout export. The sampling and filtering rules below apply the same way.

Strategy: Smart sampling, not bulk import

Don't import every email. Import the signal:

1. Sent mail (last 30 days) — who the user actively communicates with
2. Starred/important emails — user-curated signal
3. Threads with 3+ replies — active conversations worth tracking
4. Emails from people already in the brain — enrichment, not cold import

Processing

For each email thread:

1. Entity detection — extract people, companies mentioned
2. Update people pages — add communication context to timeline
3. Create meeting pages — if the email is a meeting summary or follow-up
4. Skip noise — newsletters, automated notifications, marketing

Filtering rules

Auto-skip (never import):

• noreply@, no-reply@, notifications@, support@, mailer-daemon@
• Unsubscribe-heavy senders (marketing)
• GitHub/Jira/Linear notification emails
• Calendar invites (already captured in Phase 3)

Always import:

• Direct emails from people in the brain
• Starred/flagged emails
• Emails the user sent (their words are highest-value signal)

Phase 5: Conversation Exports (ChatGPT / Claude / Perplexity)

Your thinking, captured. AI conversation exports reveal what the user was researching, building, and thinking about. This is original thinking preserved in dialog form.

Supported formats

• ChatGPT: Settings → Data Controls → Export → conversations.json
• Claude: Download from claude.ai conversation history
• Perplexity: Export from settings

Processing

For each conversation:

1. Assess significance (1-5 scale):
   • 1 = Pure utility (how-tos, quick lookups) → skip or minimal page
   • 2 = Minor context → 1-paragraph note
   • 3 = Notable (reveals interests, building something) → full page
   • 4 = Important (deep personal processing, strategic thinking) → rich page
   • 5 = Defining (identity work, breakthrough insights) → full treatment
2. Extract entities — people, companies, concepts discussed
3. Capture original thinking — the user's exact phrasing is the signal. Never paraphrase.
4. File by primary subject — not in a "conversations/" dump. A conversation about a person goes to people/, about a concept goes to concepts/, etc.

Quality rule

Only import conversations rated 3+. The brain is for signal, not noise.

Phase 6: X/Twitter Archive

Your public positions and engagement patterns. Twitter reveals what the user thinks, who they engage with, and what ideas they're developing publicly.

Data sources

1. Twitter data export (Settings → Your Account → Download Archive)
   • Contains all tweets, likes, DMs, bookmarks
2. Live API (if available) — recent tweets and engagement
3. Bookmarks — curated signal, high value

Brain structure

brain/media/x/{handle}/
├── x-log.md                     ← compiled truth (themes, voice, key threads)
├── daily/YYYY-MM-DD.md          ← daily tweet log
├── monthly/YYYY-MM.md           ← monthly rollup
└── bookmarks/                   ← saved/bookmarked content

Processing

• Original tweets → capture with full context, extract entities
• Quote tweets → capture the user's commentary + the source tweet
• Threads → reconstruct as a single narrative
• Bookmarks → high-signal curation, import with tags
• Likes — low signal, skip unless the user wants them

Phase 7: File Archives

Historical documents, old writing, photos with metadata. This is the long tail — less structured but potentially very high value (old journals, letters, early writing).

Delegate to the archive-crawler skill. It handles:

• Crawling directory structures
• Filtering for high-value content (user's own writing, not installers)
• Text extraction from PDFs, images (OCR), documents
• Entity extraction and brain page creation

Safety gate: Archive crawling can be slow and create many pages. archive-crawler is a skill, not a CLI command — it refuses to run without an explicit archive-crawler.scan_paths: allow-list in gbrain.yml. Add the archive path to the allow-list, run the skill's scan pass first, and show the user the manifest before proceeding with full ingestion.

Supported sources:

• Local directories (Dropbox sync folder, Google Drive, old hard drives)
• Cloud storage (Backblaze B2, S3) via mounted paths
• Email archives (PST, mbox, EML, Google Takeout)
• Data exports (LinkedIn, Facebook, etc.)

Phase 8: Meeting Transcripts

Deep relationship context from recorded calls. If the user has a meeting recording service (Circleback, Otter, Fireflies, Read.ai), import recent transcripts.

Delegate to meeting-ingestion skill. Key rules:

• Always pull the complete transcript, not just the AI summary
• Entity propagation is MANDATORY — every attendee gets a timeline update
• A meeting is NOT fully ingested until all entity pages are updated

Post-Bootstrap Checklist

After completing available phases:

1. Verify brain health:

   gbrain doctor --json
   gbrain stats
   

2. Test retrieval:

   gbrain query "who do I meet with most often?"
   gbrain query "what am I working on?"
   gbrain search "<person from contacts>"
   

3. Set up live sync (if not already):

   • Calendar: daily cron
   • Email: periodic sweep (4-8 hours)
   • X: daily ingest
   • Brain repo: gbrain sync --repo <path> every 5-30 minutes

4. Track state:

   // ~/.gbrain/cold-start-state.json
   {
     "started": "2026-01-15T10:00:00Z",
     "credential_gateway": "clawvisor",
     "phases_completed": [1, 2, 3, 4],
     "phases_skipped": [6, 7],
     "total_pages_created": 847,
     "total_entities_linked": 1203,
     "next_phase": 5
   }
   

5. Tell the user what to do next:

   "Your brain has N pages across people, calendar, email, and conversations. Live sync is configured for [sources]. From here:

   • The signal-detector captures entities from every conversation
   • The briefing skill can compile daily context
   • The daily-task-prep skill handles day planning
   • Say 'enrich [person]' to deep-dive any contact"

Anti-Patterns

• Giving the agent raw OAuth tokens. This is the #1 anti-pattern. An agent with raw Gmail/Calendar tokens is an uncontrolled attack surface — one prompt injection and your entire Google account is exposed. Use ClawVisor. If the user declines ClawVisor, skip to offline imports. Never offer direct OAuth as a fallback.
• Bulk importing everything without filtering. The brain is for signal, not noise. Filter out automated senders, marketing emails, utility conversations.
• Importing without entity cross-linking. Every import should detect entities and update existing brain pages. Isolated imports don't compound.
• Not gating on user consent. Every phase should be presented as a choice. The user may not want their DMs or therapy conversations imported.
• Importing everything at significance 1. Not every conversation is worth a brain page. Use the significance scale and skip utility content.
• Creating people pages for automated senders. Sentry, GitHub notifications, newsletter platforms are not people. Filter by the rules in Phase 4.

Resume Protocol

If the session is interrupted:

1. Read ~/.gbrain/cold-start-state.json
2. Skip completed phases
3. Resume from next_phase
4. The user doesn't have to repeat credential setup or re-import completed sources

Output Format

After each phase:

PHASE N COMPLETE: [source name]
================================

Pages created: N
Pages updated: N
Entities linked: N
Time elapsed: N min

Sample pages:
- people/jane-smith.md (created — 3 emails, 5 meetings)
- companies/acme-corp.md (updated — 2 new employees linked)

Next: Phase N+1 — [description]. Ready to proceed?

Tools Used

• search — check for existing pages before creating
• query — hybrid search for entity deduplication
• get_page — read existing pages for merge decisions
• put_page — create and update brain pages
• add_link — cross-reference entities
• add_timeline_entry — record events on entity timelines
• sync_brain — sync changes to the index after each phase