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ai-browser-profile-setup

Configure ai-browser-profile to extract browser data from local files on macOS.

51|5|Updated Mar 4, 2026
Search Tags:#automation#setup#macos#extraction#browser#profile
One-click install
npx skills add https://github.com/m13v/ai-browser-profile --skill ai-browser-profile-setup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-browser-profile-setup
Source: https://github.com/m13v/ai-browser-profile/tree/main/setup
Command: npx skills add https://github.com/m13v/ai-browser-profile --skill ai-browser-profile-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It streamlines the installation, configuration, and first‑time data extraction of ai-browser-profile, removing the manual steps required to collect personal browsing information on macOS.

Core Features & Use Cases

  • Automated Installation: Installs the tool via npm, creates a Python virtual environment, and resolves all core dependencies.
  • Comprehensive Extraction: Gathers autofill, login data, history, bookmarks, IndexedDB contacts, and optional semantic embeddings from supported browsers.
  • Optional Automation: Can schedule weekly extractions and automated review via macOS launchd.
  • Use Case: Quickly provision a self‑ranking SQLite memory database of a user's digital identity for downstream AI queries.

Quick Start

Ask Claude to run the ai-browser-profile setup and extract your browser profile now.

Frequently Asked Questions about ai-browser-profile-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract personal browser data from Chrome, Safari, and Firefox on macOS?▼

To extract personal browser data on macOS, you can automate the collection of autofill, login data, history, bookmarks, and IndexedDB contacts from Chrome, Safari, and Firefox using a Python virtual environment and npm dependencies.

What do I need to set up automated browser profile extraction on macOS?▼

Setting up browser profile extraction on macOS requires Node.js 16+ and Python 3.10+ to create the virtual environment, install npm dependencies, and optionally enable semantic search embeddings.

Can I schedule weekly automated browser data extraction and review?▼

Yes, you can schedule weekly automated browser data extraction and review using macOS launchd, enabling continuous collection of your browsing information into a self-ranking SQLite memory database.

How does semantic search embedding work with extracted browser history?▼

Semantic search embeddings can be optionally enabled during the browser data extraction process, allowing downstream AI queries against the generated SQLite memory database of your digital identity.

What is the best way to create a SQLite memory database from my browsing history?▼

The best way to create a SQLite memory database from browsing history is to use an automated setup tool that extracts autofill, login data, and bookmarks from local browser files into a structured format for AI queries.

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