m13v/ai-browser-profile/setup

AI Browser Profile Setup

Interactive setup wizard for ai-browser-profile. Walk the user through installation and first extraction.

When to use

• First-time setup of ai-browser-profile
• Reinstalling after a fresh machine setup
• Troubleshooting a broken installation

Prerequisites

• Node.js 16+ (for npx)
• Python 3.10+
• macOS (browser paths are macOS-specific)

Setup Flow

Run each step sequentially. After each step, print a progress status to the user so they can follow along:

[1/7] Install ............ done (12s)
[2/7] Verify ............. done (1s)
[3/7] Extract ............ running...

Step 1: Install via npm

Check if already installed:

ls ~/ai-browser-profile/extract.py 2>/dev/null && echo "FOUND" || echo "NOT_FOUND"

If NOT_FOUND, install:

npx ai-browser-profile init

This:

• Copies Python source + skills to ~/ai-browser-profile/
• Creates a Python venv at ~/ai-browser-profile/.venv/
• Installs core deps (ccl_chromium_reader, numpy)
• Symlinks skills into ~/.claude/skills/

To update code later without touching data:

npx ai-browser-profile update

Tell the user: "Installed ai-browser-profile to ~/ai-browser-profile. Python venv created, core deps installed, skills symlinked."

Step 2: Verify the installation

~/ai-browser-profile/.venv/bin/python -c "
import sys
sys.path.insert(0, '$HOME/ai-browser-profile')
from ai_browser_profile import MemoryDB
print('MemoryDB imported successfully')
"

Expected: MemoryDB imported successfully

If it fails, check:

• Python venv exists: ls ~/ai-browser-profile/.venv/bin/python
• Deps installed: ~/ai-browser-profile/.venv/bin/pip list | grep ccl

Tell the user: "Python environment verified - MemoryDB loads correctly."

Step 3: Run extraction

IMPORTANT: Run extraction in the background so you can report progress to the user. The extraction has 8 stages and logs timing for each.

cd ~/ai-browser-profile && source .venv/bin/activate && python extract.py 2>&1

This scans all detected browsers (Arc, Chrome, Brave, Edge, Safari, Firefox) and extracts:

• Autofill profiles (names, emails, phones, addresses)
• Login data (accounts per domain)
• Browser history (tools/services used)
• Bookmarks (interests, tool usage)
• IndexedDB (WhatsApp contacts)
• Local Storage (LinkedIn connections)
• Notion (workspace contacts, if configured)
• Embeddings (semantic vectors, backfilled at end)

INTERIM PROFILE: The extraction pipeline prints an interim profile after the fast steps (autofill, history, bookmarks, logins, Notion — ~1s total) but before the slow steps (WhatsApp ~10s, embeddings ~3min). As soon as you see the "Interim profile ready" log line, show the profile to the user immediately. Don't wait for WhatsApp or embeddings to finish — the profile already has all identity, email, address, payment, account, and tool data. WhatsApp only adds a contact count.

Look for this in the logs:

Interim profile ready (WhatsApp + embeddings still running):
## User Profile
**Name:** ...

Show this to the user right away, then let the extraction continue in the background. Tell them: "Here's your profile from browser data. WhatsApp contacts and semantic embeddings are still processing..."

After extraction + cleanup finish, report a final summary to the user:

Extraction complete:
  Browsers scanned: 8 profiles (Arc, Chrome, Safari, Firefox)
  Raw memories: 5,878
  After cleanup: 5,431
  Time: 54s
 
  Breakdown:
    Autofill:      0.1s  (forms, addresses, cards)
    History:       1.8s  (tools & services)
    Bookmarks:     0.4s  (interests & links)
    Logins:        2.1s  (saved accounts)
    LinkedIn:      8.7s  (connections)
    Notion:        0.1s  (contacts & pages)
    WhatsApp:     15.3s  (contacts)
    Embeddings:   22.4s  (semantic vectors)

Step 4: Verify extraction

~/ai-browser-profile/.venv/bin/python -c "
import sys, os
sys.path.insert(0, os.path.expanduser('~/ai-browser-profile'))
from ai_browser_profile import MemoryDB
mem = MemoryDB(os.path.expanduser('~/ai-browser-profile/memories.db'))
stats = mem.stats()
print(f'Total memories: {stats[\"total_memories\"]}')
print()
print(mem.profile_text())
mem.close()
"

Show the profile to the user. Check that name, email, phone, address look reasonable. If the primary email is wrong (a contact's email ranked higher), note that the review pipeline will fix this.

Step 5: Set up automation (optional)

Ask: "Do you want weekly automatic extraction + review? (y/n)"

If yes (macOS):

ln -sf ~/ai-browser-profile/launchd/com.m13v.memory-review.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.m13v.memory-review.plist

Schedule: extracts new browser data weekly, then runs Claude to review new entries.

Step 6: Summary

Print a final status card:

Setup Complete
 
  Location:     ~/ai-browser-profile
  Database:     ~/ai-browser-profile/memories.db
  Python:       ~/ai-browser-profile/.venv/bin/python
  Skills:       ~/.claude/skills/ai-browser-profile (+ 4 more)
 
  Memories:     5,431
  Embeddings:   5,431 vectors (semantic search enabled)
  Automation:   launchd weekly / not set up
 
  Try it:       Tell Claude "what's my email address"
  Update:       npx ai-browser-profile update
  Review:       /memory-review (Claude-powered cleanup)